MLOps empowers data scientists and app developers to help bring ML models to production. MLOps enables you to track / version / audit / certify / re-use every asset in your ML lifecycle and provides orchestration services to streamline managing this lifecycle.

Azure ML contains a number of asset management and orchestration services to help you manage the lifecycle of your model training & deployment workflows.

With Azure ML + Azure DevOps you can effectively and cohesively manage your datasets, experiments, models, and ML-infused applications.

• Data/model versioning != code versioning - how to version data sets as the schema and origin data change
• Digital audit trail requirements change when dealing with code + (potentially customer) data
• Model reuse is different than software reuse, as models must be tuned based on input data / scenario.
• To reuse a model you may need to fine-tune / transfer learn on it (meaning you need the training pipeline)
• Models tend to decay over time & you need the ability to retrain them on demand to ensure they remain useful in a production context.

Model reproducibility & versioning

• Track, snapshot & manage assets used to create the model
• Enable collaboration and sharing of ML pipelines

Model auditability & explainability

• Maintain asset integrity & persist access control logs
• Certify model behavior meets regulatory & adversarial standards

Model packaging & validation

• Support model portability across a variety of platforms
• Certify model performance meets functional and latency requirements

Model deployment & monitoring

• Release models with confidence
• Monitor & know when to retrain

We are committed to providing a collection of best-in-class solutions for MLOps, both in terms of well documented & fully managed cloud solutions, as well as reusable recipes which can help your organization to bootstrap its MLOps muscle.

All of our examples will be built in the open and we welcome contributions from the community!

• https://github.com/Microsoft/Recommenders
• https://github.com/MicrosoftDocs/pipelines-azureml
• https://github.com/Microsoft/MLOps_VideoAnomalyDetection

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.

When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.