AI Workflow: An end to end AI pipeline
cs-train: Contains all the data to train the model
models: Contains all pre-trained saved models for prediction
notebooks: Contains all the notebooks describing solutions and depicting visualizations
templates: Simple templates for rendering flask app
unittest: It has logger test, API test and model test for testing all the functionalities before deploying to production and for maintenance post deployment
Dockerfile: Contains all the commands a user could call on the command line to assemble the docker image.
app.py: Flask app for creating a user interface /train and /predict APIs in order to train and predict respectively
cslib.py: A collection of functions that will transform the data set into features you can use to train a model.
model.py: A module having functions for training, loading a model and making predictions
Build the Docker image and run it
~$ cd AI-workflow
~$ docker build -t capstone-project .
Check that the image is there.
~$ docker image ls
Run the container
docker run -p 4000:8080 capstone-project
Test the running app
First go to http://0.0.0.0:4000/ to ensure the app is running and accessible.
For training the model: http://0.0.0.0:4000/train
For making predictions using the model: http://0.0.0.0:4000/predict Reviewing pointers:
Unit tests for the API: unittests/ApiTests.py
Unit tests for the model: unittests/ModelTests.py
Unit tests for the logging: unittests/LoggerTests.py
Run all of the unit tests with a single script: run-tests.py
Read/write unit tests are isolated from production models and logs
APIs for training and prediction: /app.py
Data ingestion automation pipeline: /cslib.py
Multiple models comparison: notebooks/
EDA investigation with visualizations: notebooks/data_ingestion_eda_part1.ipynb
Containerization within a working Docker image: /Dockerfile
Visualization to compare the model to the baseline model: notebooks/time_series_iteration.ipynb