Kubernetes- endpoint- multi -model deployments

[devendrabogati]

Description

This pull request adds a Kubernetes endpoint multi-model deployment example. The deployment demonstrates how to deploy multiple models (supervised and unsupervised) on the same Azure Kubernetes Service (AKS) endpoint using the Azure Machine Learning SDK v2. The models include a churn classification model and a customer segmentation clustering model.
Key Features:

• Multi-model deployment (churn prediction & segmentation)
• Azure Machine Learning Kubernetes Online Endpoint
• Resource-efficient deployment using custom resource settings
• Integration of pre-trained models with a scoring script
• Conda environment for required dependencies
• Proper logging and error handling in init and run functions

Checklist

• I have read the contribution guidelines.
  • General
  • SDK
  • CLI
• I have coordinated with the docs team ([email protected]) if this PR deletes files or changes any file names or file extensions.
• Pull request includes test coverage for the included changes.
• This notebook or file is added to the CODEOWNERS file, pointing to the author or the author's team.

[devendrabogati]

@microsoft-github-policy-service agree

[devendrabogati]

Hi @sdgilley @msakande @Blackmist @ssalgadodev @lgayhardt @fbsolo-ms1
I hope you're doing well. I wanted to kindly remind about pull request. Whenever you have a moment, could you pls take a look and provide your feedback or approve it.?
Thankyou!

[devendrabogati]

Hi @msakande ,

I hope this message finds you well. I wanted to kindly follow up on this pull request and check if there's any additional information or changes you would like me to provide. I'm happy to address any concerns or feedback.

Thanks for your time and consideration!

Best regards,
Devendra Bogati