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1 | Timestamp | Which machine library do you use the most? | Which areas or modality of machine learning / science does your team work on? | Which use-cases does your team use machine learning for? | How long does it take your team to productionise a model (including data ingestion, etc)? | Which cloud platform do you use the most? | Select the top 3 biggest challenges that you face when productionising your machine learning models | For Model Registry and/or Experiment tracking, what tool do you use the most? Skip this question if you don't use any. | For Feature Store, what tool do you use the most? Skip this question if you don't use any. | For Vector Database, what tool do you use the most? Skip this question if you don't use any. | For ETL / Workflow Orchestrator, what tool do you use the most? Skip this question if you don't use any. | For Model Training and Experimentation Platform, what tool do you use the most? Skip this question if you don't use any. | For Real Time Model Serving, what tool do you use the most? Skip this question if you don't use any. | For Model Monitoring, what tool do you use the most? Skip this question if you don't use any. | For Central Data Platform / Data Lake, what tool do you use the most? Skip this question if you don't use any. | For Managed Foundation Model / LLM Api Services, what tool do you use the most? Skip this question if you don't use any. | Which industry are you in? | How large is your organisation? | How models does your organisation have in production? This can be a guesstimate and doesn't have to be an accurate number. | In 12 months from today, how many models does your organisation plan to have in production by then? This can be a guesstimate and doesn't have to be an accurate number. | Select which of the following your organisation has set up: | What approximate percentage of your models run real-time INFERENCE instead of batch? | When productionising a machine learning model, does your infrastructure enable any of the following deployment methods? | What is the "job family" of your role? | How would you describe your role? | What is your age? | What is your country of residence? | How do you self identify? | Name of your company / employer | Score | Email address |
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2 | 14/09/2024 11:55:24 | Sklearn | Causal Inference, Time Series / Forecasting | Fraud, Risk | Less than 6 months | Google Cloud Platform | Machine learning monitoring and observability, Machine learning security, Governance and domain risks | Spreadsheets | Hopsworks | Airflow | Databricks | FastAPI/Flask Wrapper | Deltalake | OpenAI | Retail | 5,000-50,000 employees | 100-1000 | 1000+ | A Developer Productivity Team which also covers machine learning, Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Risk & Governance Function, AI Inventory (Keeping track of all AI usecases and models) | Between 10% and 30% | CI/CD for continuous deployment | Software Engineer | Director/VP | 30-34 | Germany | Male | Zalando SE | 0 / 28 | |||
3 | 15/09/2024 17:17:24 | LightGBM | Text / NLP (Non-LLM), Regression | Demand Forecasting, Search, Recommender systems, Pricing | Less than 3 months | Amazon Web Services | Inconsistency of training and experimentation environments, Building production-grade machine learning and data pipelines | Weights & Biases | Airflow | Custom Built In-house tool | FastAPI/Flask Wrapper | AWS / Lakeformation | Technology | 50-250 employees | 10-20 | 21-100 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation | More than 90% | Canary Deployments, Progressive Rollouts, CI/CD for continuous deployment, Development-Staging-Production Environments | Data Scientist | Manager | 35-39 | Male | ||||||||
4 | 15/09/2024 17:18:33 | PyTorch/Lightning/Fast.ai | Image / Computer Vision | Autonomous driving | Less than 3 months | On-prem | Inconsistency of training and experimentation environments, Gaps in tooling and support for model productionisation, Access to relevant data for training | Comet | Custom Built In-house tool | Argo Workflows | Argo Workflows | Comet | Custom Built In-house tool | Transportation & warehousing | 1,000-5,000 employees | 1000+ | 1000+ | A Developer Productivity Team which also covers machine learning, Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation | More than 90% | Development-Staging-Production Environments | MLOps Engineer | Individual Contributor (Staff+) | 45-49 | Male | |||||||
5 | 15/09/2024 18:10:11 | PyTorch/Lightning/Fast.ai | Image / Computer Vision, LLMs | Search | Less than 3 months | Amazon Web Services | Access to relevant data for training, Building production-grade machine learning and data pipelines, Machine learning monitoring and observability | Spreadsheets | Weaviate | Custom Built In-house tool | LMDeploy | OpenAI | Fashion | 50-250 employees | 2-5 | 5-9 | Between 50% and 90% | MLOps Engineer | Individual Contributor (Junior to Senior) | 25-29 | Bangladesh | Male | |||||||||
6 | 15/09/2024 18:50:17 | Sklearn | LLMs, Tabular | Fraud | Less than 3 months | Amazon Web Services | Inconsistency of training and experimentation environments, Building production-grade machine learning and data pipelines, Machine learning monitoring and observability | MLFlow | AWS Step Functions | Amazon SageMaker | FastAPI/Flask Wrapper | AWS / Lakeformation | Mistral | Technology | 250-1,000 employees | 5-9 | 10-20 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation | More than 90% | Canary Deployments, Progressive Rollouts, CI/CD for continuous deployment, Development-Staging-Production Environments | MLOps Engineer | Individual Contributor (Junior to Senior) | 35-39 | Canada | Male | ||||||
7 | 15/09/2024 18:54:32 | PyTorch/Lightning/Fast.ai | LLMs | Marketing Intelligence | Less than a week | Google Cloud Platform | Machine learning security, Governance and domain risks | Custom Built In-house tool | Hopsworks | Pinecone | Technology | Less than 10 employees | |||||||||||||||||||
8 | 15/09/2024 19:33:27 | Catboost | LLMs, Recommender Systems, Tabular, Time Series / Forecasting | Demand Forecasting, Marketing Intelligence, Recommender systems, Risk, Pricing | Less than 3 months | Amazon Web Services | Access to relevant data for training | MLFlow | Custom Built In-house tool | Pinecone | Airflow | Custom Built In-house tool | FastAPI/Flask Wrapper | Custom Built In-house tool | Snowflake | OpenAI | Retail | 250-1,000 employees | 10-20 | 21-100 | Data Platform / Data Engineering Organisation | Between 10% and 30% | Progressive Rollouts, CI/CD for continuous deployment | Data Scientist | Individual Contributor (Staff+) | 40-44 | Romania | Male | Pacvue | ||
9 | 15/09/2024 20:48:52 | Sklearn | Time Series / Forecasting | Less than 3 months | Amazon Web Services | Inconsistency of training and experimentation environments, Gaps in tooling and support for model productionisation | Spreadsheets | ActiveLoop | Airflow | Custom Built In-house tool | Custom Built In-house tool | Arize AI | AWS / Lakeformation | OpenAI | Technology | 10-50 employees | 5-9 | 10-20 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation | More than 90% | Canary Deployments, A/B Tests for Models, Progressive Rollouts | Machine Learning Engineer | C-Suite | 25-29 | USA | Male | Occuspace | ||||
10 | 15/09/2024 22:20:57 | TensorFlow | Image / Computer Vision, Recommender Systems, Text / NLP (Non-LLM) | Marketing Intelligence, Search, Recommender systems | Less than a week | No cloud platform used | Showcasing business impact and business value, Machine learning security, Governance and domain risks | MLFlow | None | None | Airflow | Custom Built In-house tool | FastAPI/Flask Wrapper | Custom Built In-house tool | Snowflake | OpenAI | Data Consulting | Less than 10 employees | 10-20 | 21-100 | A Developer Productivity Team which also covers machine learning | Less than 10% | A/B Tests for Models, CI/CD for continuous deployment | Data Scientist | Individual Contributor (Junior to Senior) | 30-34 | USA | Male | Francis037 | ||
11 | 16/09/2024 03:36:19 | PyTorch/Lightning/Fast.ai | Recommender Systems, Search, Text / NLP (Non-LLM) | Recommender systems | More than a year | Google Cloud Platform | Gaps in tooling and support for model productionisation, Building production-grade machine learning and data pipelines, Machine learning monitoring and observability | Faiss | Google Cloud Vertex AI | FastAPI/Flask Wrapper | GCP / BigLake | OpenAI | Media & Entertainment | 50-250 employees | 100-1000 | 21-100 | Central Machine Learning Platform / Team | Between 10% and 30% | A/B Tests for Models, CI/CD for continuous deployment, Development-Staging-Production Environments | MLOps Engineer | Manager | 30-34 | Japan | Male | |||||||
12 | 16/09/2024 08:40:19 | PyTorch/Lightning/Fast.ai | Time Series / Forecasting | Demand Forecasting | Less than 6 months | Amazon Web Services | Lack of specialised engineers, Showcasing business impact and business value, Machine learning monitoring and observability | MLFlow | Airflow | Databricks | Deltalake | Retail | 5,000-50,000 employees | Central Machine Learning Platform / Team, AI Risk & Governance Function, AI Inventory (Keeping track of all AI usecases and models) | Less than 10% | Canary Deployments, CI/CD for continuous deployment | Machine Learning Engineer | Individual Contributor (Junior to Senior) | 30-34 | France | Male | ||||||||||
13 | 16/09/2024 13:16:10 | Sklearn | LLMs, Text / NLP (Non-LLM), Time Series / Forecasting | Search, Recommender systems, Risk, Healthcare | Less than 6 months | Azure | Inconsistency of training and experimentation environments, Access to relevant data for training, Lack of specialised engineers | Custom Built In-house tool | Custom Built In-house tool | Custom Built In-house tool | Custom Built In-house tool | Custom Built In-house tool | Custom Built In-house tool | Azure DataLake | OpenAI | Healthcare | 5,000-50,000 employees | 21-100 | 100-1000 | A Developer Productivity Team which also covers machine learning, Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation | Less than 10% | Development-Staging-Production Environments | Machine Learning Engineer | Individual Contributor (Staff+) | 40-44 | USA | Male | ||||
14 | 16/09/2024 15:15:12 | Sklearn | Image / Computer Vision, Recommender Systems | Recommender systems, Visual Detection in Video | Less than 6 months | Azure | Gaps in tooling and support for model productionisation, Building production-grade machine learning and data pipelines | Custom Built In-house tool | OpenAI | Agriculture | 1,000-5,000 employees | 2-5 | 10-20 | Progressive Rollouts, Development-Staging-Production Environments | Business / Domain Practitioner | Individual Contributor (Staff+) | 50-54 | USA | Male | ||||||||||||
15 | 16/09/2024 17:20:18 | PyTorch/Lightning/Fast.ai | Recommender Systems | Marketing Intelligence | More than a year | Amazon Web Services | Lack of specialised engineers, Lack of specialised data scientists | MLFlow | Custom Built In-house tool | Airflow | Amazon SageMaker | Custom Built In-house tool | Custom Built In-house tool | AWS / Lakeformation | Technology | 50-250 employees | 100-1000 | 100-1000 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation | More than 90% | Canary Deployments, A/B Tests for Models, Progressive Rollouts, CI/CD for continuous deployment | Individual Contributor (Staff+) | 30-34 | Israel | Male | Bigabid | |||||
16 | 16/09/2024 22:03:59 | PyTorch/Lightning/Fast.ai | Image / Computer Vision | Less than a week | Amazon Web Services | Access to relevant data for training, Showcasing business impact and business value, Building production-grade machine learning and data pipelines | Data Version Control (DVC) | Food | 10-50 employees | 5-9 | 5-9 | AI Inventory (Keeping track of all AI usecases and models) | Less than 10% | CI/CD for continuous deployment, Development-Staging-Production Environments | Machine Learning Engineer | Individual Contributor (Staff+) | 30-34 | Male | |||||||||||||
17 | 16/09/2024 23:49:22 | PyTorch/Lightning/Fast.ai | LLMs, Recommender Systems | Search, Recommender systems | Less than 3 months | Amazon Web Services | Gaps in tooling and support for model productionisation, Machine learning security, Governance and domain risks | Data Version Control (DVC) | Custom Built In-house tool | Milvus | Argo Workflows | Amazon SageMaker | Sagemaker | Neptune AI | AWS / Lakeformation | Azure AI | Telecommunications | 50,000+ employees | 100-1000 | 1000+ | A Developer Productivity Team which also covers machine learning, Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation | Between 50% and 90% | Canary Deployments, A/B Tests for Models, CI/CD for continuous deployment, Development-Staging-Production Environments | MLOps Engineer | Individual Contributor (Staff+) | 35-39 | Germany | Male | Deutsche Telekom | ||
18 | 17/09/2024 02:30:50 | Sklearn | Tabular | Fraud | More than a year | Amazon Web Services | Inconsistency of training and experimentation environments, Lack of specialised engineers | Custom Built In-house tool | Custom Built In-house tool | Custom Built In-house tool | Amazon Glue | Amazon SageMaker | Sagemaker | Custom Built In-house tool | AWS / Lakeformation | Custom Built In-house tool | Financial services | 50,000+ employees | 1000+ | 100-1000 | Central Machine Learning Platform / Team | Between 10% and 30% | CI/CD for continuous deployment | Machine Learning Engineer | Manager | 30-34 | Male | ||||
19 | 17/09/2024 03:11:21 | PyTorch/Lightning/Fast.ai | Image / Computer Vision | Pharmaceuticals | Less than 6 months | Google Cloud Platform | Inconsistency of training and experimentation environments, Gaps in tooling and support for model productionisation, Machine learning monitoring and observability | Weights & Biases | Prefect | Anyscale and also Slurm | FastAPI/Flask Wrapper | GCP / BigLake | Biotech | 250-1,000 employees | 10-20 | 21-100 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Inventory (Keeping track of all AI usecases and models) | Less than 10% | CI/CD for continuous deployment, Development-Staging-Production Environments | MLOps Engineer | Individual Contributor (Junior to Senior) | Prefer not to share | USA | Female | |||||||
20 | 17/09/2024 10:43:16 | Sklearn | Image / Computer Vision, LLMs, Tabular, Text / NLP (Non-LLM), Time Series / Forecasting | Demand Forecasting, Pricing | Less than a month | Azure | Gaps in tooling and support for model productionisation, Lack of specialised engineers, Building production-grade machine learning and data pipelines | MLFlow | Azure AI Search | Databricks | FastAPI/Flask Wrapper | Azure DataLake | Azure AI | Technology | 250-1,000 employees | 2-5 | 5-9 | A Developer Productivity Team which also covers machine learning, Data Platform / Data Engineering Organisation | Between 10% and 30% | Development-Staging-Production Environments | Machine Learning Engineer | Individual Contributor (Junior to Senior) | 25-29 | Brazil | Male | Dataside | |||||
21 | 17/09/2024 11:07:08 | Sklearn | Tabular | Demand Forecasting, Marketing Intelligence, Risk, Pricing | Less than a week | Azure | Gaps in tooling and support for model productionisation, Lack of specialised engineers, Showcasing business impact and business value | MLFlow | Custom Built In-house tool | nil | databricks | Databricks | Databricks | mlcore | Deltalake | Azure AI | consulting | 1,000-5,000 employees | 10-20 | 10-20 | A Developer Productivity Team which also covers machine learning, Central Machine Learning Platform / Team | Less than 10% | CI/CD for continuous deployment | Machine Learning Engineer | Individual Contributor (Junior to Senior) | 30-34 | India | Male | Tiger Analytics | ||
22 | 17/09/2024 14:31:35 | Sklearn | Image / Computer Vision, LLMs, Recommender Systems, Search, Text / NLP (Non-LLM), Time Series / Forecasting | Demand Forecasting, Marketing Intelligence, Search, Recommender systems | Less than 6 months | Amazon Web Services | Lack of specialised engineers, Machine learning monitoring and observability, Machine learning security | Weights & Biases | Hopsworks | Weaviate | Airflow | Amazon SageMaker | BentoML | Evidently AI | AWS / Lakeformation | OpenAI | Aerospace & defense | Less than 10 employees | 1 | A/B Tests for Models, CI/CD for continuous deployment, Development-Staging-Production Environments | Data Scientist | Individual Contributor (Junior to Senior) | 18-21 | India | Male | ||||||
23 | 17/09/2024 16:17:13 | PyTorch/Lightning/Fast.ai | LLMs, Recommender Systems, Search, Text / NLP (Non-LLM) | Search, Property Prediction (Molecular) | Less than 3 months | Amazon Web Services | Building production-grade machine learning and data pipelines, Governance and domain risks | Custom Built In-house tool | Custom Built In-house tool | Milvus | Custom Built In-house tool | Custom Built In-house tool | Custom Built In-house tool | Custom Built In-house tool | Custom Built In-house tool | Custom Built In-house tool | Technology | 50,000+ employees | 1000+ | 1000+ | A Developer Productivity Team which also covers machine learning, Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Risk & Governance Function | Between 10% and 30% | Canary Deployments, A/B Tests for Models, CI/CD for continuous deployment | Software Engineer | Director/VP | 45-49 | USA | Male | IBM / IBM Research | ||
24 | 17/09/2024 16:58:53 | Sklearn | LLMs, Text / NLP (Non-LLM), Time Series / Forecasting | Marketing Intelligence, Risk, Pricing | Less than a year | Amazon Web Services | FastAPI/Flask Wrapper | AWS / Lakeformation | Electronics | 50-250 employees | 21-100 | 21-100 | A Developer Productivity Team which also covers machine learning, AI Risk & Governance Function, AI Inventory (Keeping track of all AI usecases and models) | More than 90% | MLOps Engineer | C-Suite | 35-39 | Male | |||||||||||||
25 | 17/09/2024 20:44:31 | Dspy | LLMs, Recommender Systems, Search, Text / NLP (Non-LLM) | Marketing Intelligence, Recommender systems | Amazon Web Services | Access to relevant data for training, Showcasing business impact and business value, Machine learning monitoring and observability | MLFlow | Airflow | FastAPI/Flask Wrapper | OpenAI | 250-1,000 employees | Data Platform / Data Engineering Organisation | CI/CD for continuous deployment | Machine Learning Engineer | Individual Contributor (Junior to Senior) | ||||||||||||||||
26 | 20/09/2024 10:37:36 | PyTorch/Lightning/Fast.ai | Image / Computer Vision, LLMs, Tabular, Time Series / Forecasting | defect detection | Less than 6 months | Azure | Lack of specialised engineers, Machine learning monitoring and observability, Governance and domain risks | Weights & Biases | Dataiku | Dataiku | Azure ML Studio | Triton Inference Server | Custom Built In-house tool | Snowflake | Azure AI | Technology | 5,000-50,000 employees | 10-20 | 21-100 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Inventory (Keeping track of all AI usecases and models) | Between 50% and 90% | Development-Staging-Production Environments | Data Scientist | Individual Contributor (Junior to Senior) | 30-34 | Germany | Male | Heraeus | |||
27 | 22/09/2024 17:58:12 | XGBoost | Text / NLP (Non-LLM) | Risk | More than a year | Amazon Web Services | Inconsistency of training and experimentation environments, Access to relevant data for training, Governance and domain risks | MLFlow | FEAST | Qdrant | Dbt | Amazon SageMaker | WhyLabs | AWS / Lakeformation | Healthcare | 10-50 employees | 0 | 2-5 | CI/CD for continuous deployment | Data Scientist | Manager | ||||||||||
28 | 22/09/2024 18:16:52 | Catboost | LLMs, Recommender Systems, Tabular, Text / NLP (Non-LLM), Time Series / Forecasting | Marketing Intelligence, Recommender systems, Fraud, Risk | Less than 6 months | Azure | Access to relevant data for training, Showcasing business impact and business value | MLFlow | Custom Built In-house tool | Custom Built In-house tool | Custom Built In-house tool | Databricks | FastAPI/Flask Wrapper | NannyML | Deltalake | Azure AI | Telecommunications | 5,000-50,000 employees | 10-20 | 21-100 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Risk & Governance Function | Less than 10% | A/B Tests for Models, Progressive Rollouts, CI/CD for continuous deployment, Development-Staging-Production Environments | Data Scientist | Individual Contributor (Staff+) | 25-29 | Thailand | Male | AIS | ||
29 | 22/09/2024 19:20:06 | PyTorch/Lightning/Fast.ai | Image / Computer Vision, Search, Text / NLP (Non-LLM) | Search | Less than a month | Amazon Web Services | Lack of specialised engineers, Building production-grade machine learning and data pipelines | MLFlow | Milvus | Argo Workflows | Amazon SageMaker | Nvidia Triton Inference Server | Retail | 50-250 employees | 2-5 | 5-9 | Between 50% and 90% | Machine Learning Engineer | Individual Contributor (Junior to Senior) | 35-39 | Turkey | Male | |||||||||
30 | 22/09/2024 21:25:48 | Stable-Baselines3 | RL | Optimization | Less than a month | Amazon Web Services | Inconsistency of training and experimentation environments, Building production-grade machine learning and data pipelines, Machine learning monitoring and observability | Custom Built In-house tool | Custom Built In-house tool | Custom Built In-house tool | Custom Built In-house tool | Custom Built In-house tool | Custom Built In-house tool | Energy | Less than 10 employees | 0 | 1 | Central Machine Learning Platform / Team | More than 90% | Data Scientist | Individual Contributor (Staff+) | 25-29 | USA | Male | |||||||
31 | 22/09/2024 23:48:32 | PyTorch/Lightning/Fast.ai | LLMs, Search, Text / NLP (Non-LLM) | Search | Less than 3 months | Google Cloud Platform | Inconsistency of training and experimentation environments, Machine learning monitoring and observability, Machine learning security | MLFlow | Milvus | Argo Workflows | Custom Built In-house tool | Custom Built In-house tool | OpenAI | Technology | 50-250 employees | 5-9 | 10-20 | Central Machine Learning Platform / Team | More than 90% | Progressive Rollouts, CI/CD for continuous deployment, Development-Staging-Production Environments | Machine Learning Engineer | Individual Contributor (Junior to Senior) | 25-29 | USA | Male | ||||||
32 | 23/09/2024 00:12:18 | TensorFlow | Tabular | Marketing Intelligence, Pricing | Less than 3 months | Amazon Web Services | Gaps in tooling and support for model productionisation, Building production-grade machine learning and data pipelines | Weights & Biases | LanceDB | Metaflow | Custom Built In-house tool | FastAPI/Flask Wrapper | Evidently AI | OpenAI | Financial services | 1,000-5,000 employees | 1000+ | 1000+ | Data Platform / Data Engineering Organisation | Between 10% and 30% | Machine Learning Engineer | Individual Contributor (Staff+) | 30-34 | USA | Male | Carta | |||||
33 | 23/09/2024 01:21:05 | PyTorch/Lightning/Fast.ai | LLMs, Text / NLP (Non-LLM) | Recommender systems, Agentic systems, agents assists | Less than a month | Amazon Web Services | Inconsistency of training and experimentation environments, Access to relevant data for training, Showcasing business impact and business value | Custom Built In-house tool | Milvus | Custom Built In-house tool | FastAPI/Flask Wrapper | Snowflake | Azure AI | Telecommunications | 250-1,000 employees | 2-5 | 10-20 | Central Machine Learning Platform / Team | More than 90% | Development-Staging-Production Environments | Product Manager | Director/VP | 40-44 | Male | |||||||
34 | 23/09/2024 07:02:07 | PyTorch/Lightning/Fast.ai | LLMs, Text / NLP (Non-LLM) | Fraud | Less than a year | Google Cloud Platform | Inconsistency of training and experimentation environments, Gaps in tooling and support for model productionisation, Access to relevant data for training | Weights & Biases | FEAST | Milvus | Argo Workflows | Google Cloud Vertex AI | FastAPI/Flask Wrapper | Neptune AI | Snowflake | OpenAI | Financial services | 50-250 employees | 21-100 | 21-100 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation | Between 50% and 90% | A/B Tests for Models | Software Engineer | Manager | 25-29 | Vietnam | Male | |||
35 | 23/09/2024 08:23:30 | PyTorch/Lightning/Fast.ai | Image / Computer Vision | Fraud | Less than a month | Google Cloud Platform | Inconsistency of training and experimentation environments, Showcasing business impact and business value, Governance and domain risks | ClearML | Custom Built In-house tool | Custom Built In-house tool | Custom Built In-house tool | Retail | 10-50 employees | 10-20 | 21-100 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation | More than 90% | A/B Tests for Models, CI/CD for continuous deployment | Software Engineer | Individual Contributor (Staff+) | 40-44 | Germany | Male | ||||||||
36 | 23/09/2024 08:51:33 | Sklearn | LLMs, Tabular | Fraud | Less than a year | Amazon Web Services | Gaps in tooling and support for model productionisation, Access to relevant data for training, Machine learning monitoring and observability | Spreadsheets | Custom Built In-house tool | Custom Built In-house tool | Custom Built In-house tool | Custom Built In-house tool | OpenAI | Financial services | 250-1,000 employees | 21-100 | 21-100 | Data Platform / Data Engineering Organisation | More than 90% | Development-Staging-Production Environments | Data Scientist | Manager | 35-39 | Spain | Male | ||||||
37 | 23/09/2024 10:30:58 | Sklearn | Time Series / Forecasting | Risk | Less than 6 months | Azure | Inconsistency of training and experimentation environments, Gaps in tooling and support for model productionisation, Showcasing business impact and business value | MLFlow | Azure ML Studio | FastAPI/Flask Wrapper | Azure ML | Deltalake | Energy | 5,000-50,000 employees | 100-1000 | 1000+ | Data Platform / Data Engineering Organisation | Less than 10% | CI/CD for continuous deployment, Development-Staging-Production Environments | Machine Learning Engineer | Individual Contributor (Staff+) | 35-39 | Netherlands | Male | |||||||
38 | 23/09/2024 10:51:34 | Catboost | LLMs, Tabular | Marketing Intelligence, Support automation | Less than 3 months | Amazon Web Services | Lack of specialised engineers, Showcasing business impact and business value, Machine learning monitoring and observability | ClearML | Airflow | clearml | FastAPI/Flask Wrapper | OpenAI | Media & Entertainment | 250-1,000 employees | 2-5 | 5-9 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation | Between 10% and 30% | Development-Staging-Production Environments | Machine Learning Engineer | Manager | 35-39 | Estonia | Male | |||||||
39 | 23/09/2024 11:27:25 | PyTorch/Lightning/Fast.ai | Recommender Systems, Search | Search, Recommender systems | Google Cloud Platform | Lack of specialised engineers, Building production-grade machine learning and data pipelines | Vertex AI Experiments | Airflow | Google Cloud Vertex AI | GCP / BigLake | Travel | 250-1,000 employees | 10-20 | 21-100 | Data Platform / Data Engineering Organisation | Less than 10% | Data Scientist | Individual Contributor (Junior to Senior) | Male | ||||||||||||
40 | 23/09/2024 16:01:09 | TensorFlow | Causal Inference, Search, Time Series / Forecasting | Demand Forecasting, Search, Fraud, Risk | Less than 3 months | Azure | Access to relevant data for training, Showcasing business impact and business value, Building production-grade machine learning and data pipelines | MLFlow | Prefect | Databricks | Databricks | Deltalake | OpenAI | Financial services | Less than 10 employees | 1 | 2-5 | A Developer Productivity Team which also covers machine learning | Less than 10% | CI/CD for continuous deployment, Development-Staging-Production Environments | Data Engineer | Individual Contributor (Staff+) | 30-34 | USA | Male | Granum Technologies LLC | |||||
41 | 23/09/2024 20:02:03 | PyTorch/Lightning/Fast.ai | Image / Computer Vision, LLMs | Search, Recommender systems | Less than a week | Digital Ocean | Showcasing business impact and business value, Building production-grade machine learning and data pipelines, Machine learning security | Data Version Control (DVC) | Pgvector | Prefect | Custom Built In-house tool | SkyPilot | Evidently AI | Custom Built In-house tool | Custom Built In-house tool | Technology | Less than 10 employees | 2-5 | 5-9 | A Developer Productivity Team which also covers machine learning, Central Machine Learning Platform / Team | Between 50% and 90% | A/B Tests for Models, Progressive Rollouts, CI/CD for continuous deployment | Machine Learning Engineer | Individual Contributor (Junior to Senior) | Prefer not to share | USA | Male | ||||
42 | 23/09/2024 23:13:22 | PyTorch/Lightning/Fast.ai | Recommender Systems, Search, Text / NLP (Non-LLM) | Search, Recommender systems | Less than 6 months | Amazon Web Services | Lack of specialised engineers, Showcasing business impact and business value, Building production-grade machine learning and data pipelines | MLFlow | Opensearch | Argo Workflows | FastAPI/Flask Wrapper | Anthropic | Marketplaces | 250-1,000 employees | 1 | 2-5 | A Developer Productivity Team which also covers machine learning | More than 90% | A/B Tests for Models, Progressive Rollouts, Development-Staging-Production Environments | Machine Learning Engineer | Individual Contributor (Junior to Senior) | 25-29 | Austria | Willhaben | |||||||
43 | 24/09/2024 09:53:22 | PyTorch/Lightning/Fast.ai | LLMs, Search, Text / NLP (Non-LLM) | Search | Less than a week | Amazon Web Services | Gaps in tooling and support for model productionisation, Lack of specialised engineers, Building production-grade machine learning and data pipelines | MLFlow | Custom Built In-house tool | Airflow | Amazon SageMaker | BentoML | Custom Built In-house tool | Deltalake | Technology | 250-1,000 employees | 21-100 | 21-100 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation | Between 50% and 90% | Canary Deployments, Progressive Rollouts, CI/CD for continuous deployment, Development-Staging-Production Environments | Machine Learning Engineer | Manager | 35-39 | USA | Male | |||||
44 | 24/09/2024 10:01:35 | XGBoost | LLMs, Tabular, Text / NLP (Non-LLM), Time Series / Forecasting | Demand Forecasting, Marketing Intelligence, Pricing | Less than 6 months | Azure | Showcasing business impact and business value, Building production-grade machine learning and data pipelines, Machine learning monitoring and observability | MLFlow | selft built / Databricks | Airflow | Databricks | FastAPI/Flask Wrapper | Custom Built In-house tool | Snowflake | Azure AI | Energy | 1,000-5,000 employees | 1000+ | 1000+ | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation | Between 10% and 30% | Progressive Rollouts, CI/CD for continuous deployment, Development-Staging-Production Environments | MLOps Engineer | Manager | 40-44 | Netherlands | Male | Eneco | |||
45 | 24/09/2024 10:24:58 | Sklearn | Causal Inference, Tabular, Time Series / Forecasting | Synthetic data generation | Less than 3 months | Amazon Web Services | Lack of specialised engineers, Lack of specialised data scientists | Custom Built In-house tool | Argo Workflows | Custom Built In-house tool | Technology | 10-50 employees | 2-5 | 5-9 | Data Platform / Data Engineering Organisation | Less than 10% | A/B Tests for Models, CI/CD for continuous deployment | Product Manager | Manager | 30-34 | USA | Female | YData | ||||||||
46 | 24/09/2024 10:40:18 | Perpetual | LLMs, Tabular, Time Series / Forecasting | Fraud, Risk | Less than 3 months | Amazon Web Services | Gaps in tooling and support for model productionisation, Lack of specialised data scientists, Showcasing business impact and business value | azure ai search | Databricks | FastAPI/Flask Wrapper | Perpetual ML Suite | Snowflake | Azure AI | Financial services | 1,000-5,000 employees | 10-20 | 21-100 | Between 10% and 30% | Machine Learning Engineer | Individual Contributor (Staff+) | 35-39 | Turkey | Male | ||||||||
47 | 24/09/2024 11:24:48 | Sklearn | LLMs, Recommender Systems, Tabular, Text / NLP (Non-LLM) | Demand Forecasting, Recommender systems, Fraud, Risk, Pricing | Less than 3 months | Google Cloud Platform | Showcasing business impact and business value, Machine learning monitoring and observability | MLFlow | Airflow | Custom Built In-house tool | FastAPI/Flask Wrapper | Evidently AI | GCP / BigLake | OpenAI | Technology | 250-1,000 employees | 21-100 | 21-100 | Central Machine Learning Platform / Team | Less than 10% | Canary Deployments | Machine Learning Engineer | Individual Contributor (Junior to Senior) | 35-39 | |||||||
48 | 24/09/2024 11:29:46 | PyTorch/Lightning/Fast.ai | LLMs | Marketing Intelligence, Strategy | Less than a month | Google Cloud Platform | Inconsistency of training and experimentation environments, Machine learning monitoring and observability, Machine learning security | Custom Built In-house tool | Custom Built In-house tool | Pinecone | NiFi | Custom Built In-house tool | FastAPI/Flask Wrapper | Custom Built In-house tool | Anthropic | Technology | Less than 10 employees | 2-5 | 2-5 | A Developer Productivity Team which also covers machine learning, Data Platform / Data Engineering Organisation, AI Inventory (Keeping track of all AI usecases and models) | Less than 10% | Director/VP | |||||||||
49 | 24/09/2024 17:08:19 | Sklearn | Search, Tabular, Time Series / Forecasting | Demand Forecasting, Fraud, Risk | Less than 3 months | Amazon Web Services | Inconsistency of training and experimentation environments, Gaps in tooling and support for model productionisation | MLFlow | Airflow | Databricks | FastAPI/Flask Wrapper | Custom Built In-house tool | Snowflake | Custom Built In-house tool | Technology | 50,000+ employees | 1000+ | 1000+ | Data Platform / Data Engineering Organisation, AI Risk & Governance Function | Less than 10% | Development-Staging-Production Environments | Machine Learning Engineer | Individual Contributor (Staff+) | 25-29 | India | Male | Accenture | ||||
50 | 24/09/2024 17:51:35 | Sklearn | Tabular, Time Series / Forecasting | Risk, Pricing | Less than a month | Amazon Web Services | Inconsistency of training and experimentation environments, Access to relevant data for training | MLFlow | Custom Built In-house tool | Airflow | Custom Built In-house tool | FastAPI/Flask Wrapper | Custom Built In-house tool | Financial services | 50-250 employees | 5-9 | 5-9 | Central Machine Learning Platform / Team | Less than 10% | CI/CD for continuous deployment, Development-Staging-Production Environments | MLOps Engineer | Individual Contributor (Junior to Senior) | 45-49 | Colombia | Male | ||||||
51 | 24/09/2024 20:22:40 | PyTorch/Lightning/Fast.ai | LLMs | Search, Recommender systems | Less than 3 months | Amazon Web Services | Lack of specialised engineers, Lack of specialised data scientists, Showcasing business impact and business value | MLFlow | Databricks | Databricks | Databricks Workflows | Databricks | Databricks | Technology | 250-1,000 employees | 21-100 | 100-1000 | ||||||||||||||
52 | 24/09/2024 21:20:21 | XGBoost | LLMs, Recommender Systems, Time Series / Forecasting | Demand Forecasting, Fraud, Risk | Less than a week | Amazon Web Services | Showcasing business impact and business value | Snowflake | Snowflake | Snowflake | Snowflake | Custom Built In-house tool | Snowflake | Snowflake | Transportation & warehousing | 5,000-50,000 employees | 10-20 | 21-100 | Data Platform / Data Engineering Organisation | Less than 10% | Canary Deployments, A/B Tests for Models, Progressive Rollouts, CI/CD for continuous deployment, Development-Staging-Production Environments | Data Scientist | Individual Contributor (Staff+) | 35-39 | Germany | Male | Large Travel Company | ||||
53 | 26/09/2024 02:24:28 | PyTorch/Lightning/Fast.ai | Causal Inference, Image / Computer Vision, LLMs, Recommender Systems, Text / NLP (Non-LLM), Time Series / Forecasting | Demand Forecasting, Recommender systems, Risk, Climate Modelling, Geospatial AI | Less than a month | Amazon Web Services | Gaps in tooling and support for model productionisation, Access to relevant data for training, Governance and domain risks | Custom Built In-house tool | Custom Built In-house tool | Airflow | Databricks | Databricks | Fiddler AI | AWS / Lakeformation | Custom Built In-house tool | Insurance | 50,000+ employees | 21-100 | 100-1000 | Data Platform / Data Engineering Organisation, AI Risk & Governance Function, AI Inventory (Keeping track of all AI usecases and models) | Less than 10% | CI/CD for continuous deployment, Development-Staging-Production Environments | Business / Domain Practitioner | Director/VP | 45-49 | United Kingdom | Male | AXA | |||
54 | 26/09/2024 12:25:25 | Nixtla | Time Series / Forecasting | Demand Forecasting | Less than a month | Amazon Web Services | Inconsistency of training and experimentation environments, Lack of specialised engineers | MLFlow | Databricks | Custom Built In-house tool | Deltalake | Amazon Bedrock | Retail | 50,000+ employees | 21-100 | 100-1000 | Data Platform / Data Engineering Organisation | Between 10% and 30% | A/B Tests for Models, CI/CD for continuous deployment, Development-Staging-Production Environments | Data Scientist | Individual Contributor (Junior to Senior) | 30-34 | France | Male | Decathlon | ||||||
55 | 28/09/2024 22:38:11 | Sklearn | Causal Inference, Tabular, Time Series / Forecasting | Demand Forecasting, Recommender systems, Predictive maintenance | Less than 3 months | Azure | Inconsistency of training and experimentation environments, Machine learning monitoring and observability | MLFlow | Azure data factory, Databricks workflows, azureML pipelines | Azure ML Studio | Deltalake | Azure AI | Energy | 5,000-50,000 employees | 5-9 | 21-100 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Risk & Governance Function | Less than 10% | Canary Deployments, A/B Tests for Models, CI/CD for continuous deployment, Development-Staging-Production Environments | MLOps Engineer | Individual Contributor (Staff+) | 35-39 | Germany | Male | |||||||
56 | 29/09/2024 18:19:30 | TensorFlow | Image / Computer Vision | Image Classification | Less than 3 months | Amazon Web Services | Inconsistency of training and experimentation environments, Access to relevant data for training, Building production-grade machine learning and data pipelines | ClearML | Airflow | Amazon SageMaker | FastAPI/Flask Wrapper | AWS / Lakeformation | Utilities | 50-250 employees | 2-5 | 5-9 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation | Between 10% and 30% | Canary Deployments, A/B Tests for Models, Progressive Rollouts, Development-Staging-Production Environments | Data Scientist | Manager | 40-44 | Israel | ASTERRA | |||||||
57 | 29/09/2024 18:39:41 | TensorFlow | Causal Inference, LLMs, Text / NLP (Non-LLM), Time Series / Forecasting | Demand Forecasting, Marketing Intelligence, Fraud, Risk, Pricing | More than a year | Amazon Web Services | Inconsistency of training and experimentation environments, Gaps in tooling and support for model productionisation, Machine learning monitoring and observability | Spreadsheets | Airflow | Amazon SageMaker | Sagemaker | Custom Built In-house tool | OpenAI | Financial services | 50-250 employees | 5-9 | 10-20 | A Developer Productivity Team which also covers machine learning | Canary Deployments, A/B Tests for Models, Progressive Rollouts | Product Manager | Individual Contributor (Staff+) | ||||||||||
58 | 29/09/2024 19:35:44 | XGBoost | Tabular | Pricing | Less than 3 months | Azure | Inconsistency of training and experimentation environments, Access to relevant data for training | MLFlow | Databricks workflows | Databricks | FastAPI/Flask Wrapper | Custom Built In-house tool | Azure DataLake | Financial services | 50-250 employees | 2-5 | 5-9 | Data Platform / Data Engineering Organisation | More than 90% | CI/CD for continuous deployment, Development-Staging-Production Environments | Machine Learning Engineer | Individual Contributor (Staff+) | 30-34 | Male | |||||||
59 | 30/09/2024 01:16:17 | Sklearn | Causal Inference, Image / Computer Vision, Recommender Systems, Tabular, Text / NLP (Non-LLM), Time Series / Forecasting | Demand Forecasting, Marketing Intelligence, Recommender systems, Fraud, Risk, Pricing | Less than 6 months | Amazon Web Services | Inconsistency of training and experimentation environments, Access to relevant data for training, Lack of specialised engineers | Custom Built In-house tool | Custom Built In-house tool | Custom Built In-house tool | Custom Built In-house tool | Amazon SageMaker | Sagemaker | Custom Built In-house tool | AWS / Lakeformation | Amazon Bedrock | Financial services | 50,000+ employees | 100-1000 | 21-100 | Between 10% and 30% | CI/CD for continuous deployment | Machine Learning Engineer | Manager | 30-34 | Male | |||||
60 | 30/09/2024 09:36:26 | PyTorch/Lightning/Fast.ai | LLMs | Less than 3 months | Amazon Web Services | Access to relevant data for training, Showcasing business impact and business value, Machine learning monitoring and observability | Custom Built In-house tool | Elastic Search | Custom Built In-house tool | OpenAI | Media & Entertainment | 1,000-5,000 employees | 2-5 | ||||||||||||||||||
61 | 30/09/2024 11:43:15 | Sklearn | LLMs, Tabular, Text / NLP (Non-LLM), Time Series / Forecasting | Demand Forecasting, Search, Pricing | Less than 6 months | Amazon Web Services | Lack of specialised engineers, Showcasing business impact and business value, Machine learning monitoring and observability | MLFlow | Custom Built In-house tool | Pinecone | Kubeflow | Custom Built In-house tool | FastAPI/Flask Wrapper | Custom Built In-house tool | Snowflake | Custom Built In-house tool | Transportation & warehousing | 50,000+ employees | 21-100 | 21-100 | Central Machine Learning Platform / Team | More than 90% | Progressive Rollouts, CI/CD for continuous deployment, Development-Staging-Production Environments | Machine Learning Engineer | Individual Contributor (Staff+) | Prefer not to share | Germany | ||||
62 | 30/09/2024 14:21:41 | ||||||||||||||||||||||||||||||
63 | 30/09/2024 14:33:04 | triton | Tabular, Time Series / Forecasting | Fraud | Less than a month | Amazon Web Services | Gaps in tooling and support for model productionisation, Machine learning monitoring and observability, Governance and domain risks | Custom Built In-house tool | Custom Built In-house tool | Custom Built In-house tool | Custom Built In-house tool | Custom Built In-house tool | Custom Built In-house tool | Financial services | 250-1,000 employees | 21-100 | 100-1000 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Risk & Governance Function | More than 90% | Canary Deployments, A/B Tests for Models, Progressive Rollouts | MLOps Engineer | Manager | |||||||||
64 | 01/10/2024 18:59:05 | Sklearn | Time Series / Forecasting | Demand Forecasting, Marketing Intelligence, Recommender systems | Less than 3 months | Azure | Inconsistency of training and experimentation environments, Lack of specialised engineers, Lack of specialised data scientists | Data Version Control (DVC) | FEAST | Weaviate | Airflow | Azure ML Studio | Databricks | Evidently AI | Deltalake | Azure AI | Technology | 50-250 employees | 2-5 | 10-20 | Central Machine Learning Platform / Team | Between 10% and 30% | CI/CD for continuous deployment | Data Scientist | Manager | 30-34 | Male | ||||
65 | 02/10/2024 09:09:52 | LGBM | Causal Inference, Recommender Systems, Time Series / Forecasting | Search, Recommender systems, Fraud, Risk, Pricing | Less than 3 months | Amazon Web Services | Building production-grade machine learning and data pipelines, Machine learning monitoring and observability | Spreadsheets | Custom Built In-house tool | Argo Workflows | Custom Built In-house tool | FastAPI/Flask Wrapper | Trino | Food | 1,000-5,000 employees | 21-100 | 10-20 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation | Between 50% and 90% | Canary Deployments, A/B Tests for Models, CI/CD for continuous deployment | Machine Learning Engineer | Individual Contributor (Junior to Senior) | 40-44 | Spain | |||||||
66 | 03/10/2024 10:35:02 | ||||||||||||||||||||||||||||||
67 | 03/10/2024 11:18:44 | lightgbm | Recommender Systems | Recommender systems | Less than 3 months | Azure | Access to relevant data for training, Showcasing business impact and business value, Machine learning monitoring and observability | MLFlow | Databricks | Databricks | Databricks | Databricks | Databricks | Custom Built In-house tool | Deltalake | Azure AI | Retail | 50,000+ employees | 1000+ | 1000+ | A Developer Productivity Team which also covers machine learning | Between 50% and 90% | A/B Tests for Models, CI/CD for continuous deployment, Development-Staging-Production Environments | MLOps Engineer | Manager | 30-34 | Netherlands | Female | Ahold Delhaize | ||
68 | 03/10/2024 11:45:30 | TensorFlow | Image / Computer Vision, LLMs, Recommender Systems | Recommender systems, Computer Vision | Less than 6 months | Amazon Web Services | Access to relevant data for training, Lack of specialised engineers, Governance and domain risks | MLFlow | Opensearch AWS | None | Amazon SageMaker | Sagemaker | Grafana | AWS / Lakeformation | OpenAI | Media & Entertainment | 1,000-5,000 employees | 2-5 | 10-20 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation | Less than 10% | A/B Tests for Models, CI/CD for continuous deployment | Machine Learning Engineer | Individual Contributor (Junior to Senior) | 30-34 | Switzerland | Male | ||||
69 | 03/10/2024 12:21:23 | TensorFlow | Causal Inference, Image / Computer Vision, LLMs, Recommender Systems, Search, Tabular, Text / NLP (Non-LLM), Time Series / Forecasting | Demand Forecasting, Marketing Intelligence, Search, Recommender systems, Pricing | Less than a week | Amazon Web Services | Showcasing business impact and business value, Machine learning monitoring and observability, Governance and domain risks | MLFlow | Snowflake | DynamoDB | Airflow | Amazon SageMaker | Sagemaker | Sagemaker | AWS / Lakeformation | Azure AI | Media & Entertainment | 5,000-50,000 employees | 100-1000 | 1000+ | Data Platform / Data Engineering Organisation, AI Risk & Governance Function | Between 50% and 90% | Canary Deployments, CI/CD for continuous deployment, Development-Staging-Production Environments | Machine Learning Engineer | Individual Contributor (Staff+) | 35-39 | Netherlands | Male | DPG Media | ||
70 | 03/10/2024 12:35:07 | Sklearn | Tabular, Time Series / Forecasting | Demand Forecasting, Anomaly detection | Less than a month | Azure | Access to relevant data for training, Lack of specialised engineers, Showcasing business impact and business value | MLFlow | Databricks | Custom Built In-house tool | Azure DataLake | Energy | 10-50 employees | 2-5 | 5-9 | Data Platform / Data Engineering Organisation | Less than 10% | CI/CD for continuous deployment, Development-Staging-Production Environments | Software Engineer | Manager | 45-49 | Norway | Male | Prelect AS | |||||||
71 | 03/10/2024 13:10:43 | XGBoost | Recommender Systems, Tabular, Time Series / Forecasting | Recommender systems, Fraud, Risk, Pricing | Less than 6 months | Amazon Web Services | Access to relevant data for training, Building production-grade machine learning and data pipelines, Machine learning monitoring and observability | Amazon SageMaker | FastAPI/Flask Wrapper | Telecommunications | Less than 10 employees | 2-5 | 10-20 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation | Between 10% and 30% | Development-Staging-Production Environments | Business / Domain Practitioner | Director/VP | 50-54 | Netherlands | Male | ||||||||||
72 | 03/10/2024 20:55:09 | Sklearn | Time Series / Forecasting | Demand Forecasting | Less than 6 months | Azure | Machine learning monitoring and observability | MLFlow | Azure devops | Azure ML Studio | Azure ml | Deltalake | Utilities | 50,000+ employees | 2-5 | 5-9 | Central Machine Learning Platform / Team | Less than 10% | CI/CD for continuous deployment, Development-Staging-Production Environments | MLOps Engineer | Individual Contributor (Junior to Senior) | 30-34 | United Kingdom | Male | |||||||
73 | 04/10/2024 01:29:32 | Sklearn | Causal Inference, Recommender Systems, Time Series / Forecasting | Demand Forecasting, Recommender systems, Fraud, Risk | Less than a week | Amazon Web Services | Gaps in tooling and support for model productionisation, Showcasing business impact and business value, Governance and domain risks | Domino | Pinecone | Airflow | Domino | Seldon Core | Domino | Snowflake | Ensemble | Technology | 10-20 | 10-20 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Risk & Governance Function | Less than 10% | A/B Tests for Models, Development-Staging-Production Environments | Business / Domain Practitioner | C-Suite | 35-39 | USA | Male | |||||
74 | 04/10/2024 14:41:14 | PyTorch/Lightning/Fast.ai | LLMs, Text / NLP (Non-LLM) | translation and multilinguality | Less than 3 months | Amazon Web Services | Gaps in tooling and support for model productionisation, Access to relevant data for training, Machine learning monitoring and observability | Custom Built In-house tool | Databricks | FastAPI/Flask Wrapper | Snowflake | OpenAI | Technology | 250-1,000 employees | 100-1000 | 100-1000 | Data Platform / Data Engineering Organisation | More than 90% | CI/CD for continuous deployment, Development-Staging-Production Environments | Machine Learning Engineer | Manager | 35-39 | Czech Republic | Male | Phrase | ||||||
75 | 05/10/2024 17:41:44 | PyTorch/Lightning/Fast.ai | Image / Computer Vision, LLMs | quality assurance | Less than 6 months | Azure | Inconsistency of training and experimentation environments, Gaps in tooling and support for model productionisation, Machine learning monitoring and observability | Spreadsheets | Azure ML Studio | FastAPI/Flask Wrapper | Snowflake | Healthcare | 5,000-50,000 employees | 2-5 | 5-9 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Risk & Governance Function, AI Inventory (Keeping track of all AI usecases and models) | Between 50% and 90% | Canary Deployments, CI/CD for continuous deployment | Machine Learning Engineer | Individual Contributor (Staff+) | 40-44 | Male | |||||||||
76 | 06/10/2024 16:25:20 | Sklearn | Tabular | Fraud, Risk, Pricing | Less than a month | Amazon Web Services | Lack of specialised engineers, Building production-grade machine learning and data pipelines, Governance and domain risks | Hopsworks | Hopsworks | Hopsworks | Airflow | Hopsworks | KServe | Hopsworks | Deltalake | OpenAI | Insurance | 5,000-50,000 employees | 21-100 | 100-1000 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation | Less than 10% | A/B Tests for Models, CI/CD for continuous deployment, Development-Staging-Production Environments | Machine Learning Engineer | Individual Contributor (Staff+) | 45-49 | Northern Ireland | Male | |||
77 | 06/10/2024 17:38:22 | Sklearn | Time Series / Forecasting | Demand Forecasting, Anomaly Détection | Less than a month | Azure | Access to relevant data for training, Showcasing business impact and business value, Machine learning monitoring and observability | MLFlow | Custom Built In-house tool | Custom Built In-house tool | Energy | 50-250 employees | 100-1000 | 1000+ | Central Machine Learning Platform / Team | Less than 10% | Data Engineer | Individual Contributor (Staff+) | 30-34 | France | Male | ||||||||||
78 | 06/10/2024 18:54:54 | Sklearn | Tabular, Time Series / Forecasting | Risk | Less than 6 months | Google Cloud Platform | Building production-grade machine learning and data pipelines, Machine learning security | Data Version Control (DVC) | Abinitio | FastAPI/Flask Wrapper | Evidently AI | GCP / BigLake | Financial services | 50-250 employees | Data Scientist | Individual Contributor (Junior to Senior) | 30-34 | Male | |||||||||||||
79 | 06/10/2024 19:17:37 | PyTorch/Lightning/Fast.ai | Causal Inference, LLMs, Recommender Systems, Search | Search, Recommender systems | Less than a month | Amazon Web Services | Inconsistency of training and experimentation environments, Gaps in tooling and support for model productionisation, Showcasing business impact and business value | MLFlow | Tecton | Custom Built In-house tool | Databricks | Custom Built In-house tool | Sagemaker | Custom Built In-house tool | Deltalake | Amazon Bedrock | Technology | 5,000-50,000 employees | 100-1000 | 100-1000 | Central Machine Learning Platform / Team, AI Inventory (Keeping track of all AI usecases and models) | Between 50% and 90% | Canary Deployments, A/B Tests for Models, Progressive Rollouts, CI/CD for continuous deployment, Development-Staging-Production Environments | Machine Learning Engineer | Individual Contributor (Junior to Senior) | 30-34 | USA | Male | Redacted | ||
80 | 06/10/2024 19:44:17 | Sklearn | LLMs, Recommender Systems, Search, Text / NLP (Non-LLM) | Search, Recommender systems | Less than a month | Amazon Web Services | Access to relevant data for training, Showcasing business impact and business value, Building production-grade machine learning and data pipelines | Spreadsheets | opensearch / Azure AI Search | aws step function | Amazon SageMaker | FastAPI/Flask Wrapper | AWS / Lakeformation | Amazon Bedrock | Insurance | 5,000-50,000 employees | 10-20 | 21-100 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation | Between 50% and 90% | CI/CD for continuous deployment | Data Scientist | Individual Contributor (Staff+) | 30-34 | Italy | Male | |||||
81 | 06/10/2024 22:25:59 | Pymc | Causal Inference, Tabular, Time Series / Forecasting | Demand Forecasting, Decision support | Less than 6 months | Amazon Web Services | Access to relevant data for training | MLFlow | Databricks workflow | Databricks | Deltalake | Retail | 50-250 employees | 21-100 | 21-100 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Inventory (Keeping track of all AI usecases and models) | Less than 10% | Development-Staging-Production Environments | Data Scientist | Director/VP | 40-44 | Male | |||||||||
82 | 07/10/2024 03:50:28 | PyTorch/Lightning/Fast.ai | Tabular, Time Series / Forecasting | Fraud, Risk | Less than a month | Amazon Web Services | Building production-grade machine learning and data pipelines, Governance and domain risks | Optuna | Custom Built In-house tool | Airflow | Custom Built In-house tool | FastAPI/Flask Wrapper | Custom Built In-house tool | Custom Built In-house tool | Financial services | 250-1,000 employees | 5-9 | 5-9 | Data Platform / Data Engineering Organisation, AI Risk & Governance Function | More than 90% | Progressive Rollouts, CI/CD for continuous deployment, Development-Staging-Production Environments | Machine Learning Engineer | Individual Contributor (Junior to Senior) | 25-29 | USA | Male | |||||
83 | 07/10/2024 04:51:15 | Sklearn | LLMs, Search, Tabular, Text / NLP (Non-LLM) | Search, Digitalisation | Less than a month | Amazon Web Services | Access to relevant data for training, Unit economics | MLFlow | Custom Built In-house tool | Elasticsearch | Airflow | Databricks | Deltalake | OpenAI | Food | 50-250 employees | 1 | 2-5 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation | More than 90% | A/B Tests for Models, Progressive Rollouts, CI/CD for continuous deployment, Development-Staging-Production Environments | Data Scientist | Individual Contributor (Junior to Senior) | 30-34 | Germany | Male | Choco | ||||
84 | 07/10/2024 08:46:30 | PyTorch/Lightning/Fast.ai | Image / Computer Vision, LLMs, Recommender Systems, Search, Tabular, Text / NLP (Non-LLM) | Marketing Intelligence, Recommender systems, Fraud, Risk | Less than 3 months | Azure | Lack of specialised engineers, Machine learning monitoring and observability, Machine learning security | MLFlow | FAISS | Databricks | Databricks | FastAPI/Flask Wrapper | Custom Built In-house tool | Deltalake | OpenAI | Technology | 10-50 employees | 10-20 | 21-100 | A Developer Productivity Team which also covers machine learning, Data Platform / Data Engineering Organisation | Less than 10% | CI/CD for continuous deployment, Development-Staging-Production Environments | Data Scientist | Individual Contributor (Staff+) | 40-44 | Netherlands | Male | ||||
85 | 07/10/2024 11:26:45 | Sklearn | LLMs, Tabular, Text / NLP (Non-LLM), Time Series / Forecasting | Demand Forecasting, | Less than a month | On-prem | Access to relevant data for training, Building production-grade machine learning and data pipelines, Machine learning monitoring and observability | Google Gemini | Technology | 10-50 employees | 2-5 | 5-9 | Less than 10% | Product Manager | C-Suite | 40-44 | India | Male | |||||||||||||
86 | 07/10/2024 11:27:32 | ||||||||||||||||||||||||||||||
87 | 07/10/2024 14:58:22 | Sklearn | Causal Inference, Image / Computer Vision, LLMs, Recommender Systems, Tabular, Text / NLP (Non-LLM), Time Series / Forecasting | Demand Forecasting, Marketing Intelligence, Search, Recommender systems, Pricing | Less than 6 months | Amazon Web Services | Access to relevant data for training, Building production-grade machine learning and data pipelines, Machine learning monitoring and observability | MLFlow | Databricks | Databricks | Airflow | Databricks | Custom Built In-house tool | Custom Built In-house tool | Deltalake | OpenAI | Retail | 5,000-50,000 employees | 21-100 | 100-1000 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Risk & Governance Function | Less than 10% | A/B Tests for Models, CI/CD for continuous deployment | Machine Learning Engineer | Individual Contributor (Staff+) | 40-44 | Denmark | Female | |||
88 | 07/10/2024 22:02:58 | Sklearn | Recommender Systems, Tabular, Text / NLP (Non-LLM), Time Series / Forecasting | Demand Forecasting, Marketing Intelligence, Recommender systems, Pricing, Optimization, finance | Less than 6 months | Amazon Web Services | Access to relevant data for training, Showcasing business impact and business value, Governance and domain risks | MLFlow | Custom Built In-house tool | Dagster | Custom Built In-house tool | Custom Built In-house tool | Custom Built In-house tool | Pharma | 5,000-50,000 employees | 10-20 | 10-20 | A Developer Productivity Team which also covers machine learning | Less than 10% | Development-Staging-Production Environments | Machine Learning Engineer | Manager | 30-34 | Spain | Male | Sandoz Pharmaceuticals | |||||
89 | 09/10/2024 07:17:12 | TensorFlow | Image / Computer Vision, LLMs, Text / NLP (Non-LLM) | Marketing Intelligence, Search | Less than a month | Amazon Web Services | Lack of specialised engineers | Spreadsheets | Custom Built In-house tool | Opensearch | Custom Built In-house tool | Custom Built In-house tool | FastAPI/Flask Wrapper | Amazon Bedrock | Market Research | 50-250 employees | 10-20 | 10-20 | Central Machine Learning Platform / Team | Less than 10% | CI/CD for continuous deployment | Data Scientist | Manager | 30-34 | India | Male | |||||
90 | 09/10/2024 10:57:58 | Catboost | Recommender Systems, Tabular, Text / NLP (Non-LLM) | Recommender systems, Pricing, OCR based tasks | Less than 6 months | Google Cloud Platform | Inconsistency of training and experimentation environments, Machine learning monitoring and observability, Machine learning security | Custom Built In-house tool | FEAST | Kubeflow | Google Cloud Vertex AI | FastAPI/Flask Wrapper | GCP / BigLake | Automotive | 1,000-5,000 employees | 10-20 | 21-100 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation | Between 50% and 90% | Development-Staging-Production Environments | MLOps Engineer | Individual Contributor (Junior to Senior) | 30-34 | India | Male | ||||||
91 | 09/10/2024 11:04:10 | Catboost | Causal Inference, Recommender Systems, Tabular | Recommender systems, Pricing | Less than 6 months | Google Cloud Platform | Inconsistency of training and experimentation environments, Access to relevant data for training, Building production-grade machine learning and data pipelines | GCS and Clearml | FEAST | kubeflow and ray | Google Cloud Vertex AI | FastAPI/Flask Wrapper | GCP / BigLake | Google Gemini | Automotive | 1,000-5,000 employees | 21-100 | 21-100 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation | Between 50% and 90% | CI/CD for continuous deployment, Development-Staging-Production Environments | Machine Learning Engineer | Individual Contributor (Staff+) | 30-34 | India | Male | |||||
92 | 10/10/2024 13:19:50 | Sklearn | Tabular, Time Series / Forecasting | Demand Forecasting | Less than 6 months | Amazon Web Services | Lack of specialised engineers, Showcasing business impact and business value | MLFlow | Custom Built In-house tool | Custom Built In-house tool | Custom Built In-house tool | KServe | Evidently AI | Dremio | Technology | 10-50 employees | 2-5 | 0 | Central Machine Learning Platform / Team | Less than 10% | CI/CD for continuous deployment | MLOps Engineer | Individual Contributor (Junior to Senior) | 30-34 | Colombia | Male | Zapata ai | ||||
93 | 11/10/2024 12:41:46 | Catboost | Causal Inference, LLMs, Recommender Systems, Tabular, Time Series / Forecasting | Demand Forecasting, Marketing Intelligence, Search, Recommender systems, Fraud, Pricing | Less than a month | Amazon Web Services | Access to relevant data for training, Lack of specialised engineers, Machine learning monitoring and observability | MLFlow | Tecton | Weaviate | Prefect | Databricks | Sagemaker | Custom Built In-house tool | Custom Built In-house tool | Amazon Bedrock | Food | 5,000-50,000 employees | 21-100 | 21-100 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation | Less than 10% | CI/CD for continuous deployment, Development-Staging-Production Environments | Machine Learning Engineer | Manager | 40-44 | USA | Male | HelloFresh | ||
94 | 13/10/2024 18:30:20 | Sklearn | Recommender Systems, Search | Search, Recommender systems | Less than a month | Amazon Web Services | Gaps in tooling and support for model productionisation, Lack of specialised data scientists, Machine learning monitoring and observability | Spreadsheets | Custom Built In-house tool | Airflow | Amazon SageMaker | Sagemaker | Custom Built In-house tool | Snowflake | OpenAI | Healthcare | 50-250 employees | 5-9 | 5-9 | Data Platform / Data Engineering Organisation | More than 90% | Canary Deployments, A/B Tests for Models, Progressive Rollouts, CI/CD for continuous deployment, Development-Staging-Production Environments | Data Engineer | Director/VP | 30-34 | USA | Male | ||||
95 | 13/10/2024 22:25:40 | XGBoost | LLMs, Recommender Systems, Tabular, Text / NLP (Non-LLM), Time Series / Forecasting | Marketing Intelligence, Recommender systems, Risk, Pricing | Less than a month | Google Cloud Platform | Lack of specialised engineers, Showcasing business impact and business value, Machine learning monitoring and observability | Weights & Biases | Custom Built In-house tool | Airflow | Google Cloud Vertex AI | GCP / BigLake | Google Gemini | Telecommunications | 50,000+ employees | 100-1000 | 100-1000 | A Developer Productivity Team which also covers machine learning, Data Platform / Data Engineering Organisation, AI Risk & Governance Function | Less than 10% | CI/CD for continuous deployment, Development-Staging-Production Environments | Business / Domain Practitioner | Manager | 45-49 | United Kingdom | Male | Vodafone | |||||
96 | 14/10/2024 02:16:05 | PyTorch/Lightning/Fast.ai | LLMs | Less than a year | Amazon Web Services | Inconsistency of training and experimentation environments, Access to relevant data for training, Lack of specialised engineers | MLFlow | Custom Built In-house tool | Milvus | Argo Workflows | Custom Built In-house tool | Custom Built In-house tool | Custom Built In-house tool | Custom Built In-house tool | Custom Built In-house tool | Media & Entertainment | 5,000-50,000 employees | 2-5 | 5-9 | A Developer Productivity Team which also covers machine learning | Between 10% and 30% | Canary Deployments | Machine Learning Engineer | Individual Contributor (Staff+) | 55-59 | Japan | Male | NIKE | |||
97 | 14/10/2024 10:35:47 | PyTorch/Lightning/Fast.ai | Image / Computer Vision, 3D data (volumetric, mesh...) | Data analysis and enhancement | More than a year | Amazon Web Services | Access to relevant data for training, Lack of specialised engineers, Lack of specialised data scientists | ClearML | ClearML | Custom Built In-house tool | FastAPI/Flask Wrapper | Healthcare | 10-50 employees | 5-9 | 10-20 | Central Machine Learning Platform / Team | Machine Learning Engineer | Individual Contributor (Junior to Senior) | 25-29 | Female | |||||||||||
98 | 15/10/2024 18:06:42 | XGBoost | Causal Inference, Tabular, Time Series / Forecasting | Demand Forecasting | Less than a month | Azure | Access to relevant data for training, Showcasing business impact and business value, Machine learning monitoring and observability | Custom Built In-house tool | Custom Built In-house tool | Custom Built In-house tool | Custom Built In-house tool | Custom Built In-house tool | Insurance | 50-250 employees | 5-9 | 10-20 | Less than 10% | A/B Tests for Models, Development-Staging-Production Environments | Data Scientist | Individual Contributor (Junior to Senior) | 35-39 | USA | Male | ||||||||
99 | 16/10/2024 11:10:42 | Sklearn | Causal Inference, LLMs, Recommender Systems, Text / NLP (Non-LLM), Time Series / Forecasting, Reinforcement Learning | Demand Forecasting, Marketing Intelligence, Search, Recommender systems, Fraud, Risk, Pricing | Less than 3 months | Azure | Gaps in tooling and support for model productionisation, Machine learning monitoring and observability | MLFlow | Custom Built In-house tool | Chroma | Custom Built In-house tool | Azure ML Studio | FastAPI/Flask Wrapper | Custom Built In-house tool | Azure DataLake | OpenAI | Consultancy | 50-250 employees | 21-100 | 100-1000 | A Developer Productivity Team which also covers machine learning | Between 10% and 30% | Canary Deployments, A/B Tests for Models, Progressive Rollouts, CI/CD for continuous deployment, Development-Staging-Production Environments | Machine Learning Engineer | Individual Contributor (Staff+) | 35-39 | United Kingdom | Non-binary | Datasparq | ||
100 | 18/10/2024 17:46:02 | Nixtla, darts, sktime, gluonts | Causal Inference, Time Series / Forecasting | Demand Forecasting | Less than a week | Azure | Machine learning monitoring and observability | Custom Built In-house tool | Custom Built In-house tool | Argo Workflows | Kubernetes, docker | Kubernetes | NannyML | TDengine | OpenAI | Technology | Less than 10 employees | 1000+ | 1000+ | A Developer Productivity Team which also covers machine learning | Less than 10% | Machine Learning Engineer | Director/VP | 50-54 | France | Male | |||||
101 | 20/10/2024 18:25:16 | PyTorch/Lightning/Fast.ai | Tabular, Text / NLP (Non-LLM) | Drug Discovery | Less than a month | Amazon Web Services | Gaps in tooling and support for model productionisation, Lack of specialised engineers, Machine learning monitoring and observability | MLFlow | Custom Built In-house tool | Argo Workflows | Custom Built In-house tool | KServe | AWS / Lakeformation | Amazon Bedrock | Healthcare | 50-250 employees | 1000+ | 1000+ | A Developer Productivity Team which also covers machine learning, Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Inventory (Keeping track of all AI usecases and models) | More than 90% | Canary Deployments, A/B Tests for Models, Progressive Rollouts, CI/CD for continuous deployment, Development-Staging-Production Environments | MLOps Engineer | Director/VP | 40-44 | United Kingdom | Male | |||||
102 | 20/10/2024 19:10:04 | XGBoost | LLMs, Recommender Systems | Search, Recommender systems | Less than a month | Google Cloud Platform | Inconsistency of training and experimentation environments, Showcasing business impact and business value | Weights & Biases | Custom Built In-house tool | gcp | Airflow | Google Cloud Vertex AI | Seldon Core | Arize AI | GCP / BigLake | fireworks | Technology | 50-250 employees | 2-5 | 5-9 | Central Machine Learning Platform / Team | Between 50% and 90% | A/B Tests for Models | Machine Learning Engineer | Individual Contributor (Staff+) | 35-39 | United Kingdom | Male | Sourcegraph | ||
103 | 20/10/2024 21:26:18 | Sklearn | LLMs, Recommender Systems, Tabular, Text / NLP (Non-LLM), Time Series / Forecasting | Demand Forecasting, Marketing Intelligence, Recommender systems | Less than 3 months | Google Cloud Platform | Inconsistency of training and experimentation environments, Machine learning monitoring and observability | Custom Built In-house tool | FEAST | Pgvector | Airflow | Custom Built In-house tool | FastAPI/Flask Wrapper | GCP / BigLake | Azure AI | Saas | 50-250 employees | 10-20 | 21-100 | Between 50% and 90% | CI/CD for continuous deployment, Development-Staging-Production Environments | Machine Learning Engineer | Individual Contributor (Junior to Senior) | 35-39 | Spain | Male | |||||
104 | 20/10/2024 22:34:50 | PyTorch/Lightning/Fast.ai | Image / Computer Vision, LLMs, Text / NLP (Non-LLM) | Fraud, Insurance | Less than 3 months | Amazon Web Services | Inconsistency of training and experimentation environments, Gaps in tooling and support for model productionisation | Weights & Biases | Custom Built In-house tool | Meta flow | Amazon SageMaker | Sagemaker | Custom Built In-house tool | AWS / Lakeformation | OpenAI | Insurance | 50-250 employees | 100-1000 | 1000+ | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Inventory (Keeping track of all AI usecases and models) | Between 50% and 90% | Canary Deployments, A/B Tests for Models | Machine Learning Engineer | Director/VP | 35-39 | United Kingdom | Male | Tractable | |||
105 | 21/10/2024 04:06:23 | PyTorch/Lightning/Fast.ai | LLMs, Text / NLP (Non-LLM) | Classification | Less than 3 months | Amazon Web Services | Inconsistency of training and experimentation environments, Showcasing business impact and business value, Machine learning monitoring and observability | MLFlow | Milvus | Databricks | FastAPI/Flask Wrapper | Deltalake | Amazon Bedrock | Technology | 5,000-50,000 employees | Machine Learning Engineer | Individual Contributor (Junior to Senior) | 25-29 | USA | Male | |||||||||||
106 | 21/10/2024 10:27:47 | TensorFlow | Image / Computer Vision, LLMs, Recommender Systems, Time Series / Forecasting | Demand Forecasting, Marketing Intelligence, Recommender systems, Fraud | Less than 3 months | Google Cloud Platform | Inconsistency of training and experimentation environments, Gaps in tooling and support for model productionisation, Building production-grade machine learning and data pipelines | MLFlow | Hopsworks | Pinecone | Airflow | Google Cloud Vertex AI | KServe | Evidently AI | GCP / BigLake | Google Gemini | Financial services | 50,000+ employees | 21-100 | 21-100 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation | Between 50% and 90% | A/B Tests for Models, Progressive Rollouts, CI/CD for continuous deployment, Development-Staging-Production Environments | MLOps Engineer | Individual Contributor (Staff+) | 30-34 | Netherlands | Male | ING Bank | ||
107 | 21/10/2024 17:13:32 | PyTorch/Lightning/Fast.ai | Image / Computer Vision, LLMs, Text / NLP (Non-LLM) | Recommender systems, Fraud | Less than 3 months | Amazon Web Services | Inconsistency of training and experimentation environments, Gaps in tooling and support for model productionisation, Access to relevant data for training | Weights & Biases | Custom Built In-house tool | Custom Built In-house tool | Amazon SageMaker | Sagemaker | Custom Built In-house tool | AWS / Lakeformation | Amazon Bedrock | Insurance | 50-250 employees | 100-1000 | 1000+ | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation | Between 50% and 90% | Canary Deployments, A/B Tests for Models | Machine Learning Engineer | Director/VP | 35-39 | United Kingdom | Male | Tractable | |||
108 | 21/10/2024 17:22:01 | PyTorch/Lightning/Fast.ai | Image / Computer Vision, LLMs, Recommender Systems, Search | Search, Recommender systems | Less than 3 months | Azure | Gaps in tooling and support for model productionisation, Building production-grade machine learning and data pipelines, Governance and domain risks | MLFlow | Elasticsearch | Azure ML pipelines | Azure ML Studio | Azure Managed Online Endpoints | Custom Built In-house tool | Azure AI | Public Sector | 1,000-5,000 employees | 10-20 | 21-100 | Between 50% and 90% | Development-Staging-Production Environments | Machine Learning Engineer | Individual Contributor (Staff+) | 30-34 | ||||||||
109 | 21/10/2024 22:29:47 | gpflow | Causal Inference, Recommender Systems, Tabular, Time Series / Forecasting | Demand Forecasting, Marketing Intelligence, process optimization | More than a year | Amazon Web Services | Gaps in tooling and support for model productionisation, Access to relevant data for training, Building production-grade machine learning and data pipelines | MLFlow | Custom Built In-house tool | None | Airflow | Custom Built In-house tool | FastAPI/Flask Wrapper | AWS / Lakeformation | None | Automotive | 5,000-50,000 employees | 21-100 | 21-100 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation | Between 50% and 90% | MLOps Engineer | Individual Contributor (Junior to Senior) | 35-39 | Luxembourg | Male | |||||
110 | 22/10/2024 09:19:51 | XGBoost | Tabular, Time Series / Forecasting | Demand Forecasting, Cost, stock, project duration forecasting | Less than a month | Amazon Web Services | Lack of specialised engineers, Lack of specialised data scientists | MLFlow | Custom Built In-house tool | None | Airflow | Custom Built In-house tool | FastAPI/Flask Wrapper | Evidently AI | AWS / Lakeformation | Custom Built In-house tool | Consultancy | 250-1,000 employees | 5-9 | 10-20 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation | Less than 10% | Canary Deployments, Progressive Rollouts, CI/CD for continuous deployment, Development-Staging-Production Environments | MLOps Engineer | Individual Contributor (Junior to Senior) | 30-34 | Spain | Male | Aily | ||
111 | 22/10/2024 10:09:33 | Sklearn | Tabular | Fraud | Less than 3 months | Amazon Web Services | Building production-grade machine learning and data pipelines, Machine learning monitoring and observability, Inconsistency of training and production environments | Custom Built In-house tool | Custom Built In-house tool | Airflow | Custom Built In-house tool | Sagemaker | Snowflake | Financial services | 50-250 employees | 10-20 | 2-5 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation | More than 90% | Data Scientist | Individual Contributor (Staff+) | 25-29 | Germany | Male | |||||||
112 | 22/10/2024 10:18:05 | Non | Image / Computer Vision, LLMs | Digitalisation | Less than 3 months | Amazon Web Services | Building production-grade machine learning and data pipelines | PostgreSQL | AWS lambda | Google Gemini | Technology | Less than 10 employees | 1 | 1 | A Developer Productivity Team which also covers machine learning | Between 10% and 30% | Software Engineer | Individual Contributor (Staff+) | 25-29 | India | Male | Omara technology | |||||||||
113 | 22/10/2024 11:08:26 | Sklearn | Causal Inference, Recommender Systems, Tabular, Time Series / Forecasting | Demand Forecasting, Marketing Intelligence, Recommender systems, Fraud, Risk, Pricing | Less than 3 months | Amazon Web Services | Gaps in tooling and support for model productionisation, Access to relevant data for training, Machine learning monitoring and observability | MLFlow | Airflow | Custom Built In-house tool | FastAPI/Flask Wrapper | Technology | 5,000-50,000 employees | 100-1000 | 1000+ | A Developer Productivity Team which also covers machine learning, Data Platform / Data Engineering Organisation, AI Risk & Governance Function, AI Inventory (Keeping track of all AI usecases and models) | Less than 10% | CI/CD for continuous deployment, Development-Staging-Production Environments | Machine Learning Engineer | Individual Contributor (Staff+) | 35-39 | Male | |||||||||
114 | 22/10/2024 12:07:49 | PyTorch/Lightning/Fast.ai | LLMs, Text / NLP (Non-LLM) | Recommender systems | Less than 3 months | Amazon Web Services | Access to relevant data for training, Building production-grade machine learning and data pipelines, Machine learning security | Healthcare | 10-50 employees | 0 | 1 | A Developer Productivity Team which also covers machine learning | Less than 10% | Software Engineer | Individual Contributor (Junior to Senior) | 22-24 | india | Male | |||||||||||||
115 | 22/10/2024 12:22:25 | ||||||||||||||||||||||||||||||
116 | 22/10/2024 12:22:52 | ||||||||||||||||||||||||||||||
117 | 22/10/2024 15:09:39 | PyTorch/Lightning/Fast.ai | Text / NLP (Non-LLM) | Recommender systems | Less than 6 months | Amazon Web Services | Gaps in tooling and support for model productionisation, Building production-grade machine learning and data pipelines, Machine learning monitoring and observability | Sagemaker | Fennel | Pinecone | Airflow | Amazon SageMaker | Sagemaker | AWS / Lakeformation | Anthropic | Hospitality | Less than 10 employees | 2-5 | 5-9 | A Developer Productivity Team which also covers machine learning, Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation | Between 10% and 30% | CI/CD for continuous deployment | MLOps Engineer | Individual Contributor (Staff+) | 45-49 | Netherlands | Male | Adaleo | |||
118 | 22/10/2024 17:40:03 | Sklearn | Tabular | Risk | Less than 6 months | Amazon Web Services | Lack of specialised data scientists, Showcasing business impact and business value | MLFlow | Custom Built In-house tool | No vector database | Airflow | Custom Built In-house tool | FastAPI/Flask Wrapper | Model metrics monitoring on Metabase | Snowflake | Amazon Bedrock | Financial services | 10-50 employees | 2-5 | 1 | Central Machine Learning Platform / Team | More than 90% | A/B Tests for Models, Progressive Rollouts, CI/CD for continuous deployment | Data Scientist | Director/VP | 30-34 | France | Male | Finfrog | ||
119 | 22/10/2024 19:27:29 | Sklearn | Recommender Systems, Time Series / Forecasting | Demand Forecasting, Recommender systems | More than a year | Azure | Inconsistency of training and experimentation environments, Showcasing business impact and business value, Building production-grade machine learning and data pipelines | MLFlow | Custom Built In-house tool | Azure Cognitive Search | Databricks Workflows | Databricks | FastAPI/Flask Wrapper | Deltalake | Azure AI | Retail | 50,000+ employees | 5-9 | 10-20 | Data Platform / Data Engineering Organisation | Between 10% and 30% | Canary Deployments, A/B Tests for Models, CI/CD for continuous deployment, Development-Staging-Production Environments | Machine Learning Engineer | Individual Contributor (Junior to Senior) | 25-29 | Netherlands | Male | Ahold Delhaize | |||
120 | 22/10/2024 22:23:07 | Sklearn | Causal Inference, LLMs, Recommender Systems, Tabular, Text / NLP (Non-LLM), Time Series / Forecasting | Demand Forecasting, Marketing Intelligence, Recommender systems, Risk | Less than 6 months | Databricks | Gaps in tooling and support for model productionisation, Machine learning monitoring and observability, Governance and domain risks | MLFlow | Custom Built In-house tool | ElasticSearch | Custom Built In-house tool | Databricks | FastAPI/Flask Wrapper | Snowflake | Azure AI | Human Resources | 50,000+ employees | 10-20 | 5-9 | Central Machine Learning Platform / Team | Less than 10% | CI/CD for continuous deployment | Data Scientist | Manager | 30-34 | USA | Male | ||||
121 | 23/10/2024 01:05:37 | Langchain | LLMs | Agents | Less than 3 months | Amazon Web Services | Inconsistency of training and experimentation environments, Building production-grade machine learning and data pipelines, Machine learning monitoring and observability | Weights & Biases | None | Pgvector | Airflow | Weights and biases | FastAPI/Flask Wrapper | Arize AI | Snowflake | OpenAI | Technology | 50-250 employees | 2-5 | 5-9 | Data Platform / Data Engineering Organisation | Between 50% and 90% | None | Machine Learning Engineer | Individual Contributor (Junior to Senior) | 25-29 | USA | Male | Vivun | ||
122 | 23/10/2024 08:48:41 | TensorFlow | Time Series / Forecasting | Meteorology | Less than a year | Amazon Web Services | Lack of specialised engineers, Machine learning monitoring and observability | MLFlow | Custom Built In-house tool | snakemake | Custom Built In-house tool | Technology | 250-1,000 employees | Central Machine Learning Platform / Team, AI Inventory (Keeping track of all AI usecases and models) | Data Scientist | Individual Contributor (Junior to Senior) | 25-29 | Switzerland | Male | MeteoSwiss | |||||||||||
123 | 23/10/2024 13:36:01 | PyTorch/Lightning/Fast.ai | Time Series / Forecasting | Risk | Less than 3 months | On-prem | Inconsistency of training and experimentation environments, Access to relevant data for training, Machine learning monitoring and observability | MLFlow | Custom Built In-house tool | Custom Built In-house tool | Airflow | Custom Built In-house tool | FastAPI/Flask Wrapper | Neptune AI | Custom Built In-house tool | Enviroment | 250-1,000 employees | 10-20 | 21-100 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation | More than 90% | CI/CD for continuous deployment | Data Scientist | Individual Contributor (Junior to Senior) | 30-34 | Switzerland | Male | ||||
124 | 23/10/2024 20:27:46 | Sklearn | Image / Computer Vision, LLMs, Recommender Systems, Search, Tabular, Text / NLP (Non-LLM), Time Series / Forecasting | Marketing Intelligence, Search, Fraud | Less than 3 months | Azure | Lack of specialised engineers, Lack of specialised data scientists, Machine learning monitoring and observability | MLFlow | None | ChromaDB | Custom Built In-house tool | Custom Built In-house tool | FastAPI/Flask Wrapper | Evidently AI | Deltalake | OpenAI | Consulting | 1,000-5,000 employees | 21-100 | 21-100 | A Developer Productivity Team which also covers machine learning, Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Risk & Governance Function | Less than 10% | A/B Tests for Models | Machine Learning Engineer | Director/VP | 35-39 | France | Male | Keyrus | ||
125 | 23/10/2024 22:30:55 | Catboost | Image / Computer Vision, LLMs, Tabular, Time Series / Forecasting | Defect detection | Less than a month | Amazon Web Services | Access to relevant data for training, Lack of specialised data scientists, Building production-grade machine learning and data pipelines | MLFlow | Databricks | Databricks | Airflow | Databricks | Databricks | Custom Built In-house tool | Deltalake | OpenAI | Manufacturing | 1,000-5,000 employees | 5-9 | 10-20 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation | More than 90% | A/B Tests for Models, CI/CD for continuous deployment, Development-Staging-Production Environments | MLOps Engineer | Individual Contributor (Staff+) | 30-34 | |||||
126 | 26/10/2024 10:15:10 | PyTorch/Lightning/Fast.ai | Image / Computer Vision, LLMs, Search | Search, Augmentation | Less than 3 months | Amazon Web Services | Gaps in tooling and support for model productionisation, Lack of specialised engineers, Building production-grade machine learning and data pipelines | Weights & Biases | Elasticsearch / Opensearch | Amazon Glue | Custom Built In-house tool | FastAPI/Flask Wrapper | OpenAI | Technology | Less than 10 employees | 1 | 2-5 | CI/CD for continuous deployment | Machine Learning Engineer | Individual Contributor (Staff+) | 30-34 | United Kingdom | Female | ||||||||
127 | 27/10/2024 18:31:56 | ||||||||||||||||||||||||||||||
128 | 27/10/2024 21:01:03 | PyTorch/Lightning/Fast.ai | Image / Computer Vision, Recommender Systems, Tabular | Recommender systems | Less than 3 months | On-prem | Inconsistency of training and experimentation environments, Gaps in tooling and support for model productionisation | ClearML | FEAST | Milvus | kubeflow pipelines | kubeflow | KServe | Custom Built In-house tool | Snowflake | Custom Built In-house tool | Media & Entertainment | 250-1,000 employees | 10-20 | 10-20 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation | Between 50% and 90% | A/B Tests for Models, Progressive Rollouts, CI/CD for continuous deployment | Machine Learning Engineer | Individual Contributor (Junior to Senior) | ||||||
129 | 27/10/2024 21:04:27 | XGBoost | Image / Computer Vision, Tabular, Text / NLP (Non-LLM) | Fraud, Risk | Less than 3 months | Google Cloud Platform | Access to relevant data for training, Lack of specialised data scientists, Showcasing business impact and business value | MLFlow | Airflow | Google Cloud Vertex AI | Spring Boot | Custom Built In-house tool | Custom Built In-house tool | Retail | 1,000-5,000 employees | 21-100 | 10-20 | A Developer Productivity Team which also covers machine learning | Less than 10% | Progressive Rollouts, CI/CD for continuous deployment, Development-Staging-Production Environments | Machine Learning Engineer | Individual Contributor (Junior to Senior) | 30-34 | Netherlands | Male | ||||||
130 | 28/10/2024 08:11:03 | ||||||||||||||||||||||||||||||
131 | 28/10/2024 09:14:43 | XGBoost | Image / Computer Vision, Tabular, Text / NLP (Non-LLM), Time Series / Forecasting | Demand Forecasting, Fraud, Risk, Customer Satisfaction | More than a year | Azure | Gaps in tooling and support for model productionisation, Lack of specialised engineers, Governance and domain risks | MLFlow | Databricks | Databricks | Databricks | FastAPI/Flask Wrapper | Custom Built In-house tool | Azure DataLake | OpenAI | Government | 5,000-50,000 employees | 5-9 | 10-20 | Data Platform / Data Engineering Organisation | Between 10% and 30% | CI/CD for continuous deployment, Development-Staging-Production Environments | Data Scientist | Individual Contributor (Staff+) | 35-39 | UK | Male | Transport for London | |||
132 | 28/10/2024 10:11:23 | TensorFlow | Recommender Systems, Search | Search, Recommender systems | Less than 6 months | GCP and AWS equally | Gaps in tooling and support for model productionisation, Showcasing business impact and business value, Big organisational migrations (of Datasets, MLP and Data platforms and mroe) | Google MetaDataStore | Custom Built In-house tool | Custom Built In-house tool | Airflow | Google Cloud Vertex AI | Sagemaker | Evidently AI | GCP / BigLake | Food Delivery | 5,000-50,000 employees | 100-1000 | 100-1000 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Risk & Governance Function | Less than 10% | Canary Deployments, A/B Tests for Models, Progressive Rollouts, CI/CD for continuous deployment, Development-Staging-Production Environments | Machine Learning Engineer | Individual Contributor (Junior to Senior) | 30-34 | Netherlands | Male | - | |||
133 | 28/10/2024 11:10:59 | langchain | Image / Computer Vision, LLMs | Search, Recommender systems, Chatbot | Less than 3 months | Amazon Web Services | Showcasing business impact and business value, Building production-grade machine learning and data pipelines, Machine learning monitoring and observability | MLFlow | Custom Built In-house tool | MongoDB | Airflow | Databricks | Databricks | Custom Built In-house tool | Deltalake | Azure AI | Retail | 5,000-50,000 employees | 21-100 | 21-100 | A Developer Productivity Team which also covers machine learning, Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Risk & Governance Function | Between 10% and 30% | A/B Tests for Models, CI/CD for continuous deployment | Machine Learning Engineer | Individual Contributor (Junior to Senior) | 25-29 | Spain | Male | Mango | ||
134 | 28/10/2024 15:52:25 | Sklearn | LLMs | Search | Less than a month | Azure | Gaps in tooling and support for model productionisation, Lack of specialised engineers, Machine learning monitoring and observability | MLFlow | Custom Built In-house tool | Pgvector | Custom Built In-house tool | Azure DataLake | Azure AI | Transportation & warehousing | 50,000+ employees | 10-20 | 5-9 | A Developer Productivity Team which also covers machine learning, Data Platform / Data Engineering Organisation, AI Risk & Governance Function | Between 10% and 30% | Progressive Rollouts | MLOps Engineer | Individual Contributor (Junior to Senior) | 30-34 | portugal | Male | ||||||
135 | 28/10/2024 17:11:50 | XGBoost | Causal Inference, Tabular, Time Series / Forecasting | Fraud, Risk | Less than a year | Access to relevant data for training, Showcasing business impact and business value, Machine learning monitoring and observability | Custom Built In-house tool | Custom Built In-house tool | FastAPI/Flask Wrapper | Custom Built In-house tool | Media & Entertainment | 250-1,000 employees | 5-9 | 10-20 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation | Between 10% and 30% | A/B Tests for Models | Data Scientist | Manager | Male | |||||||||||
136 | 03/11/2024 18:37:45 | Sklearn | LLMs, Recommender Systems, Tabular, Text / NLP (Non-LLM), Time Series / Forecasting | Demand Forecasting, Marketing Intelligence, Recommender systems, Pricing | Less than 6 months | Databricks | Access to relevant data for training, Machine learning monitoring and observability | MLFlow | Databricks | Airflow | Databricks | Databricks | Evidently AI | Deltalake | OpenAI | Media & Entertainment | 1,000-5,000 employees | 10-20 | 21-100 | Data Platform / Data Engineering Organisation, AI Inventory (Keeping track of all AI usecases and models) | Between 10% and 30% | Canary Deployments, A/B Tests for Models, Progressive Rollouts, CI/CD for continuous deployment | Machine Learning Engineer | Manager | 35-39 | Canada | Male | CBC | |||
137 | 03/11/2024 18:42:08 | PyTorch/Lightning/Fast.ai | Image / Computer Vision | Less than a month | Amazon Web Services | Inconsistency of training and experimentation environments, Gaps in tooling and support for model productionisation, Building production-grade machine learning and data pipelines | Amazon SageMaker | Sagemaker | Healthcare | Less than 10 employees | Male | ||||||||||||||||||||
138 | 03/11/2024 19:24:45 | PyTorch/Lightning/Fast.ai | Image / Computer Vision, LLMs | Search, Information extraction | Less than 6 months | Amazon Web Services | Gaps in tooling and support for model productionisation, Machine learning monitoring and observability, Governance and domain risks | Custom Built In-house tool | Prefect | Custom Built In-house tool | Consultancy | 50-250 employees | 2-5 | 2-5 | Central Machine Learning Platform / Team | Less than 10% | CI/CD for continuous deployment, Development-Staging-Production Environments | Machine Learning Engineer | Individual Contributor (Junior to Senior) | 30-34 | germany | Male | Codegaia | ||||||||
139 | 04/11/2024 05:50:24 | PyTorch/Lightning/Fast.ai | Recommender Systems | Recommender systems | Less than 3 months | Amazon Web Services | Gaps in tooling and support for model productionisation, Lack of specialised engineers | Weights & Biases | Elasticsearch | Custom Built In-house tool | Custom Built In-house tool | Sagemaker | GCP / BigLake | OpenAI | Media & Entertainment | 1,000-5,000 employees | 2-5 | 2-5 | A Developer Productivity Team which also covers machine learning | Between 50% and 90% | A/B Tests for Models, Progressive Rollouts, CI/CD for continuous deployment, Development-Staging-Production Environments | Machine Learning Engineer | Individual Contributor (Junior to Senior) | 30-34 | Japan | Male | |||||
140 | 04/11/2024 06:41:41 | Sklearn | LLMs, Tabular, Text / NLP (Non-LLM), Time Series / Forecasting | Demand Forecasting, Fraud, Risk, Pricing | Less than 3 months | Amazon Web Services | Inconsistency of training and experimentation environments, Machine learning monitoring and observability, Governance and domain risks | Weights & Biases | Custom Built In-house tool | Pinecone | Amazon Glue | Amazon SageMaker | FastAPI/Flask Wrapper | Neptune AI | Snowflake | Amazon Bedrock | Technology | 50,000+ employees | 100-1000 | 1000+ | A Developer Productivity Team which also covers machine learning, Data Platform / Data Engineering Organisation, AI Risk & Governance Function | Between 50% and 90% | Canary Deployments, CI/CD for continuous deployment, Development-Staging-Production Environments | Data Analyst | Individual Contributor (Junior to Senior) | 40-44 | India | Male | TCS | ||
141 | 04/11/2024 08:02:56 | PyTorch/Lightning/Fast.ai | Image / Computer Vision | robotics | Less than 3 months | Google Cloud Platform | Access to relevant data for training, Lack of specialised data scientists, Machine learning monitoring and observability | ClearML | Airflow | Custom Built In-house tool | tensorrt | Custom Built In-house tool | Anthropic | Transportation & warehousing | Less than 10 employees | 2-5 | 2-5 | Central Machine Learning Platform / Team | More than 90% | CI/CD for continuous deployment, Development-Staging-Production Environments | Machine Learning Engineer | Individual Contributor (Staff+) | 30-34 | netherlands | Male | Captain AI | |||||
142 | 04/11/2024 09:11:56 | Sklearn | LLMs, Tabular, Text / NLP (Non-LLM), Time Series / Forecasting | Demand Forecasting, Marketing Intelligence, Pricing | Less than a year | IBM | Inconsistency of training and experimentation environments, Lack of specialised engineers, Machine learning monitoring and observability | MLFlow | Custom Built In-house tool | Weaviate | Custom Built In-house tool | Domino | FastAPI/Flask Wrapper | Custom Built In-house tool | IBM | Azure AI | Financial services | 5,000-50,000 employees | 10-20 | 21-100 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation | Between 10% and 30% | Canary Deployments, A/B Tests for Models, CI/CD for continuous deployment | Data Scientist | Individual Contributor (Staff+) | 30-34 | FRANCE | Female | |||
143 | 04/11/2024 09:42:41 | XGBoost | LLMs, Recommender Systems, Tabular | Search, Recommender systems, content generation | Less than 6 months | Amazon Web Services | Gaps in tooling and support for model productionisation, Access to relevant data for training, Showcasing business impact and business value | Spreadsheets | Custom Built In-house tool | Airflow | Custom Built In-house tool | Custom Built In-house tool | Snowflake | OpenAI | Media & Entertainment | 50-250 employees | 2-5 | 5-9 | A Developer Productivity Team which also covers machine learning, Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation | Between 10% and 30% | A/B Tests for Models | Machine Learning Engineer | Individual Contributor (Junior to Senior) | 25-29 | Finland | Male | |||||
144 | 04/11/2024 11:46:51 | XGBoost | Tabular | Fraud, Risk, Pricing | Less than 6 months | Azure | Lack of specialised data scientists, Machine learning monitoring and observability, Governance and domain risks | Custom Built In-house tool | Argo Workflows | Custom Built In-house tool | FastAPI/Flask Wrapper | Financial services | 10-50 employees | 2-5 | 5-9 | Central Machine Learning Platform / Team, AI Inventory (Keeping track of all AI usecases and models) | Less than 10% | Development-Staging-Production Environments | Machine Learning Engineer | Director/VP | 45-49 | Israel | Male | ||||||||
145 | 06/11/2024 10:50:59 | Sklearn | Tabular | Less than a month | Azure | Access to relevant data for training, Lack of specialised engineers, Machine learning monitoring and observability | MLFlow | Prefect | Azure ML Studio | Custom Built In-house tool | Azure DataLake | Technology | 1,000-5,000 employees | 21-100 | 100-1000 | Less than 10% | CI/CD for continuous deployment, Development-Staging-Production Environments | MLOps Engineer | Individual Contributor (Junior to Senior) | 30-34 | United Kingdom | Male | |||||||||
146 | 08/11/2024 11:57:49 | PyTorch/Lightning/Fast.ai | Recommender Systems | Recommender systems | Less than a month | Google Cloud Platform | Gaps in tooling and support for model productionisation | MLFlow | Custom Built In-house tool | Qdrant | databricks | databricks | triton | Arize AI | Deltalake | Retail | 1,000-5,000 employees | 21-100 | 100-1000 | Central Machine Learning Platform / Team | Between 50% and 90% | A/B Tests for Models | Machine Learning Engineer | Individual Contributor (Junior to Senior) | 25-29 | india | Male | ||||
147 | 10/11/2024 19:35:51 | PyTorch/Lightning/Fast.ai | Text / NLP (Non-LLM) | Fraud | Less than 3 months | Google Cloud Platform | Gaps in tooling and support for model productionisation, Access to relevant data for training, Building production-grade machine learning and data pipelines | MLFlow | Hopsworks | Pinecone | Airflow | Amazon SageMaker | FastAPI/Flask Wrapper | Neptune AI | GCP / BigLake | Anthropic | Financial services | 10-50 employees | 2-5 | 5-9 | A Developer Productivity Team which also covers machine learning | Between 10% and 30% | Progressive Rollouts | Machine Learning Engineer | Manager | 30-34 | UAE | Male | |||
148 | 10/11/2024 20:35:24 | TensorFlow | LLMs, Tabular, Text / NLP (Non-LLM) | Demand Forecasting, Pricing, Document entity extraction | Less than a month | Amazon Web Services | Inconsistency of training and experimentation environments, Showcasing business impact and business value, Model consistency (re-training / catastrophic forgetting) | Weights & Biases | Custom Built In-house tool | LanceDB / Turbopuffer | Metaflow | Custom Built In-house tool | FastAPI/Flask Wrapper | Evidently AI | Snowflake | OpenAI | Financial services | 1,000-5,000 employees | 1000+ | 100-1000 | A Developer Productivity Team which also covers machine learning, Data Platform / Data Engineering Organisation, AI Inventory (Keeping track of all AI usecases and models) | Less than 10% | Progressive Rollouts | Machine Learning Engineer | Individual Contributor (Staff+) | 30-34 | United States | Male | |||
149 | 11/11/2024 01:24:04 | PyTorch/Lightning/Fast.ai | LLMs | . | Less than a month | On-prem | Gaps in tooling and support for model productionisation, Access to relevant data for training, Showcasing business impact and business value | Weights & Biases | Pinecone | Argo Workflows | Custom Built In-house tool | Custom Built In-house tool | Custom Built In-house tool | Custom Built In-house tool | Custom Built In-house tool | Media & Entertainment | 250-1,000 employees | 21-100 | 0 | AI Inventory (Keeping track of all AI usecases and models) | Between 10% and 30% | Canary Deployments | MLOps Engineer | Manager | 55-59 | Japan | Male | . | |||
150 | 11/11/2024 17:42:27 | PyTorch/Lightning/Fast.ai | LLMs | Recommender systems | Less than 6 months | Google Cloud Platform | Showcasing business impact and business value, Building production-grade machine learning and data pipelines, Governance and domain risks | Custom Built In-house tool | Seldon Core | Telecommunications | 10-50 employees | Central Machine Learning Platform / Team | Between 10% and 30% | MLOps Engineer | Manager | Male | Seldon | ||||||||||||||
151 | 11/11/2024 22:12:23 | Catboost | Causal Inference, LLMs, Tabular | Recommender systems, Risk, Text Summarization | Less than a month | Amazon Web Services | Inconsistency of training and experimentation environments, Gaps in tooling and support for model productionisation, Lack of specialised data scientists | MLFlow | DynamoDB | NA | Airflow | Local | FastAPI/Flask Wrapper | NannyML | AWS / Lakeformation | OpenAI | Healthcare | 250-1,000 employees | 2-5 | 5-9 | Data Platform / Data Engineering Organisation | Between 50% and 90% | Canary Deployments, Progressive Rollouts, CI/CD for continuous deployment | Data Scientist | Individual Contributor (Junior to Senior) | 30-34 | Brazil | Male | Alice Health | ||
152 | 12/11/2024 06:28:31 | PyTorch/Lightning/Fast.ai | Causal Inference, Image / Computer Vision | Av | Less than 3 months | On-prem | Lack of specialised engineers, Lack of specialised data scientists, Building production-grade machine learning and data pipelines | Comet | Custom Built In-house tool | Argo Workflows | Custom Built In-house tool | AWS / Lakeformation | Transportation & warehousing | 1,000-5,000 employees | 100-1000 | 1000+ | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation | Progressive Rollouts | Product Manager | Director/VP | 45-49 | Male | |||||||||
153 | 12/11/2024 09:58:12 | Sklearn | Tabular, Time Series / Forecasting | Demand Forecasting, Marketing Intelligence, Sustainability | Azure | Showcasing business impact and business value, Governance and domain risks | Azure ML Service, Sagemaker | Azure ML Studio | Evidently AI | OpenAI | Consulting | 10-50 employees | A Developer Productivity Team which also covers machine learning, Data Platform / Data Engineering Organisation | Less than 10% | Data Scientist | Individual Contributor (Junior to Senior) | |||||||||||||||
154 | 12/11/2024 14:15:35 | TensorFlow | Recommender Systems, Search | Search, Recommender systems | Less than a month | AWS & GCP | Gaps in tooling and support for model productionisation, Machine learning monitoring and observability | Custom Built In-house tool | DynamoDB | Custom Built In-house tool | Airflow | Google Cloud Vertex AI | Sagemaker | GCP / BigLake | Amazon Bedrock | Food | 250-1,000 employees | 2-5 | 5-9 | Central Machine Learning Platform / Team | Less than 10% | Development-Staging-Production Environments | Machine Learning Engineer | Individual Contributor (Staff+) | Male | ||||||
155 | 13/11/2024 09:20:17 | XGBoost | Causal Inference, Recommender Systems, Text / NLP (Non-LLM) | Recommender systems | Less than a year | Azure | Access to relevant data for training, Showcasing business impact and business value, Governance and domain risks | MLFlow | FEAST | Airflow | Custom Built In-house tool | FastAPI/Flask Wrapper | Evidently AI | Healthcare | 1,000-5,000 employees | 2-5 | 2-5 | Central Machine Learning Platform / Team | More than 90% | CI/CD for continuous deployment, Development-Staging-Production Environments | Machine Learning Engineer | Individual Contributor (Junior to Senior) | 35-39 | Netherlands | Female | ||||||
156 | 13/11/2024 10:41:51 | lightgbm | LLMs, Recommender Systems, Time Series / Forecasting | Demand Forecasting, Recommender systems, Pricing | Less than 6 months | Google Cloud Platform | Lack of specialised data scientists, Building production-grade machine learning and data pipelines, Machine learning monitoring and observability | Custom Built In-house tool | Weaviate | Airflow | Google Cloud Vertex AI | FastAPI/Flask Wrapper | Custom Built In-house tool | GCP / BigLake | OpenAI | Retail | 50-250 employees | 10-20 | 21-100 | A Developer Productivity Team which also covers machine learning, Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation | Less than 10% | A/B Tests for Models, CI/CD for continuous deployment, Development-Staging-Production Environments | Data Scientist | Individual Contributor (Staff+) | 30-34 | Germany | Male | synvert Datadrivers | |||
157 | 13/11/2024 13:31:39 | PyTorch/Lightning/Fast.ai | LLMs, Recommender Systems, Text / NLP (Non-LLM) | Recommender systems, Fraud, Risk | Less than 6 months | Azure | Inconsistency of training and experimentation environments, Access to relevant data for training, Machine learning monitoring and observability | MLFlow | Azure ai search | Custom Built In-house tool | Databricks | Databricks | Custom Built In-house tool | Azure DataLake | Azure AI | Healthcare | 5,000-50,000 employees | 10-20 | 21-100 | A Developer Productivity Team which also covers machine learning, Data Platform / Data Engineering Organisation, AI Risk & Governance Function | Less than 10% | A/B Tests for Models, CI/CD for continuous deployment | Director/VP | 45-49 | Male | ||||||
158 | 13/11/2024 14:36:42 | Sklearn | Graphs | Fraud | Less than 6 months | Amazon Web Services | Inconsistency of training and experimentation environments, Building production-grade machine learning and data pipelines | Custom Built In-house tool | Custom Built In-house tool | Argo Workflows | Databricks | FastAPI/Flask Wrapper | Custom Built In-house tool | AWS / Lakeformation | OpenAI | Financial services | 5,000-50,000 employees | 100-1000 | 100-1000 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Risk & Governance Function, AI Inventory (Keeping track of all AI usecases and models) | More than 90% | A/B Tests for Models, Progressive Rollouts, CI/CD for continuous deployment | Data Scientist | Manager | 35-39 | Brazil | Male | ||||
159 | 13/11/2024 17:29:02 | PyTorch/Lightning/Fast.ai | LLMs, Text / NLP (Non-LLM) | Search | Weights & Biases | Custom Built In-house tool | Technology | 50-250 employees | Central Machine Learning Platform / Team | Machine Learning Engineer | Individual Contributor (Junior to Senior) | 22-24 | The Netherlands | Male | |||||||||||||||||
160 | 13/11/2024 22:18:40 | Sklearn | Causal Inference, Text / NLP (Non-LLM), Time Series / Forecasting | Demand Forecasting | Less than a month | Azure | Lack of specialised engineers, Building production-grade machine learning and data pipelines | Airflow | MSSQL | Azure AI | Healthcare | 5,000-50,000 employees | 2-5 | 5-9 | Data Platform / Data Engineering Organisation, AI Risk & Governance Function, AI Inventory (Keeping track of all AI usecases and models) | Less than 10% | Data Scientist | Individual Contributor (Junior to Senior) | 30-34 | US | Male | ||||||||||
161 | 17/11/2024 17:05:49 | Catboost | Time Series / Forecasting | Demand Forecasting, Marketing Intelligence, Recommender systems, Fraud, Risk | Less than a week | Google Cloud Platform | Inconsistency of training and experimentation environments, Gaps in tooling and support for model productionisation, Showcasing business impact and business value | MLFlow | FEAST | Argo Workflows | Custom Built In-house tool | FastAPI/Flask Wrapper | Custom Built In-house tool | Custom Built In-house tool | Financial services | 1,000-5,000 employees | 100-1000 | 1000+ | Data Platform / Data Engineering Organisation | Machine Learning Engineer | Individual Contributor (Junior to Senior) | 30-34 | Male | ||||||||
162 | 17/11/2024 17:18:53 | XGBoost | Time Series / Forecasting | Fraud, Risk | Less than a month | Amazon Web Services | Building production-grade machine learning and data pipelines, Governance and domain risks | MLFlow | Airflow | Metaflow | FastAPI/Flask Wrapper | Arize AI | Deltalake | Financial services | 1,000-5,000 employees | 21-100 | 100-1000 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Risk & Governance Function | More than 90% | Canary Deployments, A/B Tests for Models, Progressive Rollouts, CI/CD for continuous deployment, Development-Staging-Production Environments | MLOps Engineer | Individual Contributor (Junior to Senior) | 30-34 | United States | Male | Upstart | |||||
163 | 17/11/2024 18:10:50 | PyTorch/Lightning/Fast.ai | Image / Computer Vision, Text / NLP (Non-LLM), Time Series / Forecasting | Demand Forecasting, Generating synthetic data | Less than 3 months | Google Cloud Platform | Gaps in tooling and support for model productionisation, Building production-grade machine learning and data pipelines, Machine learning monitoring and observability | Spreadsheets | Github actions | Google Cloud Vertex AI | FastAPI/Flask Wrapper | GCP / BigLake | Energy | 10-50 employees | 2-5 | 5-9 | Central Machine Learning Platform / Team | More than 90% | CI/CD for continuous deployment | Data Scientist | Individual Contributor (Junior to Senior) | 30-34 | United Kingdom | Male | Centre for Net Zero | ||||||
164 | 17/11/2024 18:16:15 | XGBoost | Causal Inference, LLMs, Recommender Systems, Search, Tabular, Text / NLP (Non-LLM), Time Series / Forecasting | Demand Forecasting, Marketing Intelligence, Recommender systems, Fraud, Risk, Pricing | Less than a week | Azure | Inconsistency of training and experimentation environments, Lack of specialised engineers, Machine learning monitoring and observability | omega-ml | Custom Built In-house tool | Postgresql | Celery | Omega-ml | Omega-ml | Omega-ml | Custom Built In-house tool | Omega-ml | Financial services | 250-1,000 employees | 5-9 | 10-20 | Central Machine Learning Platform / Team, AI Risk & Governance Function, AI Inventory (Keeping track of all AI usecases and models) | More than 90% | Canary Deployments, A/B Tests for Models, Progressive Rollouts, CI/CD for continuous deployment, Development-Staging-Production Environments | MLOps Engineer | Individual Contributor (Staff+) | 50-54 | Switzerland | Male | |||
165 | 18/11/2024 04:30:55 | PyTorch/Lightning/Fast.ai | Image / Computer Vision, Search, Text / NLP (Non-LLM) | Search, biotech use cases | Less than 6 months | Google Cloud Platform | Gaps in tooling and support for model productionisation, Machine learning monitoring and observability, Governance and domain risks | Weights & Biases | Airflow | Custom Built In-house tool | Anyscale/Ray | GCP / BigLake | Google Gemini | Pharmaceuticals | 250-1,000 employees | 21-100 | 100-1000 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation | Less than 10% | Development-Staging-Production Environments | MLOps Engineer | Individual Contributor (Junior to Senior) | Prefer not to share | Female | |||||||
166 | 18/11/2024 10:16:55 | PyTorch/Lightning/Fast.ai | Causal Inference, Image / Computer Vision, LLMs, Tabular, Text / NLP (Non-LLM) | Marketing Intelligence, Fraud, ICR/OCR | Less than 3 months | Azure | Inconsistency of training and experimentation environments, Gaps in tooling and support for model productionisation, Building production-grade machine learning and data pipelines | Custom Built In-house tool | Custom Built In-house tool | Azure ML Studio | FastAPI/Flask Wrapper | OpenAI | Insurance | 1,000-5,000 employees | 21-100 | 21-100 | Central Machine Learning Platform / Team, AI Inventory (Keeping track of all AI usecases and models) | Between 10% and 30% | CI/CD for continuous deployment | Data Scientist | Manager | 40-44 | Portugal | Male | Fidelidade | ||||||
167 | 24/11/2024 17:11:19 | PyTorch/Lightning/Fast.ai | LLMs, Text / NLP (Non-LLM) | Education | Less than 6 months | On-prem | Access to relevant data for training, Machine learning monitoring and observability | Weights & Biases | Google Cloud Vertex AI | Google Gemini | Technology | 1,000-5,000 employees | 10-20 | Central Machine Learning Platform / Team | Less than 10% | Software Engineer | Individual Contributor (Staff+) | 45-49 | Spain | Female | Oviedo university | ||||||||||
168 | 24/11/2024 17:52:59 | PyTorch/Lightning/Fast.ai | Text / NLP (Non-LLM) | More than a year | Amazon Web Services | Lack of specialised engineers, Building production-grade machine learning and data pipelines, Machine learning monitoring and observability | Custom Built In-house tool | Milvus | Custom Built In-house tool | Amazon SageMaker | Nvidia Triton Inference Server | Retail | 50-250 employees | 2-5 | 5-9 | More than 90% | CI/CD for continuous deployment | Machine Learning Engineer | Individual Contributor (Junior to Senior) | 35-39 | Turkey | Male | |||||||||
169 | 25/11/2024 05:59:56 | PyTorch/Lightning/Fast.ai | LLMs, Search, Text / NLP (Non-LLM) | Search, Fraud, Risk | Less than 3 months | Amazon Web Services | Machine learning monitoring and observability, Governance and domain risks, client requirements | MLFlow | opensearch, elasticsearch | Airflow | Databricks | FastAPI/Flask Wrapper | Deltalake | Azure AI | Financial services | 5,000-50,000 employees | 100-1000 | 100-1000 | Data Platform / Data Engineering Organisation, AI Risk & Governance Function | Between 10% and 30% | Machine Learning Engineer | Director/VP | 45-49 | USA | Male | ||||||
170 | 25/11/2024 22:50:18 | PyTorch/Lightning/Fast.ai | Image / Computer Vision, Search | Search | Less than 6 months | Google Cloud Platform | Gaps in tooling and support for model productionisation, Lack of specialised engineers, Lack of specialised data scientists | MLFlow | kubeflow | Google Cloud Vertex AI | TorchServe | OpenAI | 250-1,000 employees | 10-20 | 5-9 | Central Machine Learning Platform / Team | Between 50% and 90% | CI/CD for continuous deployment, Development-Staging-Production Environments | MLOps Engineer | Individual Contributor (Junior to Senior) | 25-29 | Male | |||||||||
171 | 01/12/2024 19:39:57 | PyTorch/Lightning/Fast.ai | Tabular, Time Series / Forecasting | Demand Forecasting, Pricing | Less than 6 months | Amazon Web Services | Gaps in tooling and support for model productionisation, Lack of specialised engineers, Machine learning monitoring and observability | MLFlow | Pinecone | Airflow | Databricks | FastAPI/Flask Wrapper | Evidently AI | Deltalake | Anthropic | Retail | 1,000-5,000 employees | 10-20 | 2-5 | A Developer Productivity Team which also covers machine learning, Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Inventory (Keeping track of all AI usecases and models) | Between 10% and 30% | A/B Tests for Models, CI/CD for continuous deployment, Development-Staging-Production Environments | Data Scientist | Individual Contributor (Staff+) | 25-29 | France | Male | ... | |||
172 | 01/12/2024 21:28:10 | Sklearn | Text / NLP (Non-LLM) | Demand Forecasting, Marketing Intelligence, Recommender systems, Fraud, Risk, Pricing, speech recognition, document (pii) anonymisation | Less than a year | Azure | Showcasing business impact and business value, lack of vision (what happens after productionising), reservedness from stakeholders | MLFlow | none | azure's offering in ai search | Celery | Azure ML Studio | Azure ML | Evidently AI | Azure DataLake | Azure AI | Insurance | 5,000-50,000 employees | 2-5 | 10-20 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Risk & Governance Function, AI Inventory (Keeping track of all AI usecases and models) | Between 10% and 30% | Canary Deployments, A/B Tests for Models, Progressive Rollouts, CI/CD for continuous deployment | MLOps Engineer | Individual Contributor (Staff+) | 35-39 | nl | Male | |||
173 | 02/12/2024 00:54:10 | PyTorch/Lightning/Fast.ai | LLMs, Search, Text / NLP (Non-LLM) | Search | Less than 6 months | Amazon Web Services | Gaps in tooling and support for model productionisation, Building production-grade machine learning and data pipelines, Machine learning monitoring and observability | Spreadsheets | OpenSearch | Custom Built In-house tool | Amazon SageMaker | FastAPI/Flask Wrapper | Custom Built In-house tool | Amazon Bedrock | Financial services | 1,000-5,000 employees | 10-20 | 10-20 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Risk & Governance Function | Between 10% and 30% | A/B Tests for Models, CI/CD for continuous deployment | Machine Learning Engineer | Individual Contributor (Junior to Senior) | 50-54 | USA | Male | |||||
174 | 02/12/2024 12:28:56 | LightGBM | Tabular | Fraud | Less than 6 months | Amazon Web Services | Gaps in tooling and support for model productionisation, Access to relevant data for training, Building production-grade machine learning and data pipelines | Airflow | Custom Built In-house tool | Custom Built In-house tool | Custom Built In-house tool | Financial services | 250-1,000 employees | 100-1000 | 21-100 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation, AI Risk & Governance Function | More than 90% | A/B Tests for Models, Development-Staging-Production Environments | MLOps Engineer | Individual Contributor (Junior to Senior) | 22-24 | Male | |||||||||
175 | 02/12/2024 13:01:12 | LightGBM | Time Series / Forecasting | Fraud, Risk | Less than a month | Amazon Web Services | Inconsistency of training and experimentation environments, Access to relevant data for training, Machine learning monitoring and observability | Airflow | Custom Built In-house tool | Custom Built In-house tool | Custom Built In-house tool | Financial services | 250-1,000 employees | 21-100 | 21-100 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation | More than 90% | Progressive Rollouts, Development-Staging-Production Environments | Software Engineer | Individual Contributor (Junior to Senior) | 35-39 | Portugal | Male | Feedzai | |||||||
176 | 08/12/2024 11:31:01 | Lightgbm | Recommender Systems, Search | Search, Recommender systems | Less than a month | Amazon Web Services | Inconsistency of training and experimentation environments, Machine learning monitoring and observability | Custom Built In-house tool | Custom Built In-house tool | Airflow | Amazon SageMaker | Custom Built In-house tool | Custom Built In-house tool | GCP / BigLake | Technology | 250-1,000 employees | 5-9 | 10-20 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation | Between 10% and 30% | Canary Deployments, A/B Tests for Models, Progressive Rollouts, CI/CD for continuous deployment | Machine Learning Engineer | Individual Contributor (Staff+) | 35-39 | Australia | Male | LE | ||||
177 | 13/01/2025 15:06:43 | Sklearn | LLMs, Text / NLP (Non-LLM) | Risk, Classification | Less than a month | Amazon Web Services | Inconsistency of training and experimentation environments, Building production-grade machine learning and data pipelines, Machine learning monitoring and observability | MLFlow | Prefect | Amazon SageMaker | Sagemaker | Custom Built In-house tool | Amazon Bedrock | Healthcare | 50-250 employees | 100-1000 | 100-1000 | Central Machine Learning Platform / Team, AI Inventory (Keeping track of all AI usecases and models) | Between 50% and 90% | A/B Tests for Models, CI/CD for continuous deployment, Development-Staging-Production Environments | Machine Learning Engineer | Manager | 30-34 | India | Male | ||||||
178 | 15/01/2025 22:37:25 | Sklearn | Search, Time Series / Forecasting | Search | Less than 6 months | Azure | Gaps in tooling and support for model productionisation, Showcasing business impact and business value, Machine learning monitoring and observability | MLFlow | Custom Built In-house tool | Databricks | Custom Built In-house tool | Custom Built In-house tool | Azure DataLake | Transportation & warehousing | 250-1,000 employees | 5-9 | 10-20 | Central Machine Learning Platform / Team, Data Platform / Data Engineering Organisation | Between 10% and 30% | Development-Staging-Production Environments | Machine Learning Engineer | Individual Contributor (Staff+) | 35-39 | Canada | Male |