- Lab
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Libraries: If you want this lab, consider one of these libraries.
- Data
Monitor and Automate a Model Pipeline
In this guided Azure Machine Learning lab, you will build a two-step ML pipeline that automates data loading and model training, configure compute resources and reusable assets, submit and monitor the pipeline, and inspect model lineage and run metadata in Azure ML Studio.
Lab Info
Table of Contents
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Challenge
Understand How to Define a Reproducible ML Pipeline in Azure Machine Learning
- Define a multi-step ML pipeline with separate data loading and training steps.
- Explain how pipeline components and registered assets support reuse and reproducibility.
- Understand how Azure ML tracks inputs, outputs, and execution context.
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Challenge
Demonstrate How to Submit and Monitor an Automated ML Pipeline
- Submit an ML pipeline using an Azure ML compute cluster.
- Monitor pipeline execution and interpret pipeline status and graphs.
- Demonstrate end-to-end automation without manual intervention.
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Challenge
Inspect Pipeline Outputs, Model Registration, and Lineage
- Inspect the registered model and its version created by the pipeline.
- Trace dataset, environment, and code lineage for the trained model.
- Explain how lineage supports reproducibility and auditability.
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