Which registry should I use for MLOps?

Question

Hi there,

I want to set up MLOps CI/CD pipeline, which registry should I use for MLOps(Azure Container Registry or Azure Machine Learning Registry)? And what are the best practices for MLOps?

Thanks®ards,

zmsoft

Answer 1

@zmsoft Welcome to Microsoft Q&A Forum, Thank you for posting your query here!

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For MLOps, you should use the Azure Machine Learning Registry. This registry is specifically designed for managing machine learning assets, such as models, environments, and components, and supports cross-workspace MLOps. It provides features tailored to the needs of machine learning workflows, making it easier to share and promote assets across different environments like development, testing, and production. For more info, please refer this article:

https://learn.microsoft.com/en-us/azure/machine-learning/concept-machine-learning-registries-mlops?view=azureml-api-2

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The Azure Container Registry is more general-purpose and is typically used for storing and managing container images. While it can be used in MLOps for storing containerized models, the Azure Machine Learning Registry offers more specialized capabilities for machine learning asset management.

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Here are some best practices for MLOps on Azure:

1. Implement CI/CD for MLOps: Use Azure Pipelines to automate the continuous integration and continuous deployment of your machine learning models. This helps in maintaining consistency and reliability in your deployments

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2. Reusable Software Environments: Create and use reusable environments for training and deploying models. This ensures that your builds are reproducible and consistent

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3. Model Registration and Versioning: Register your models in the Azure Machine Learning workspace to keep track of different versions and associated metadata. This helps in managing the lifecycle of your models effectively

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4. Automate Machine Learning Pipelines: Use Azure Machine Learning pipelines to automate the steps in your model training process, such as data preparation, feature extraction, and model evaluation

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5. Monitor and Alert: Set up monitoring and alerts for your machine learning models to track performance, detect data drift, and receive notifications on lifecycle events

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6. Controlled Rollout: Use controlled rollout strategies for deploying models to production. This includes A/B testing and gradual traffic shifting to ensure smooth transitions and minimize disruptions . More info here and here.

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Hope this helps. If you have any follow-up questions, please let me know. I would be happy to help.

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