Machine learning operations

Free study notes

Sections from the books on machine learning operations, free to read:

• Azure Machine Learning workspaces, registries and asset types

  When to use a workspace, a registry, models, environments, components and data assets. AI-300

• Data assets and MLTable in Azure Machine Learning

  uri_file, uri_folder and mltable data assets, and how mltable.load() finds the MLTable file. AI-300

• Tracking experiments with MLflow in Azure Machine Learning

  Experiments, runs, parameters, metrics, artifacts and autologging. AI-300

• Hyperparameter tuning with sweep jobs

  Search spaces, sampling methods and early termination policies. AI-300

• Safe rollout and rollback on managed online endpoints

  Blue-green deployments, traffic splitting, mirroring and instant rollback. AI-300

• Choosing evaluators for generative AI quality and safety

  Groundedness, relevance, coherence, fluency and risk and safety evaluators, and what each needs. AI-300

• Microsoft Foundry deployment types: Global, Data Zone and Standard

  Where prompts are processed for each deployment type, and which one meets data residency needs. AI-300

Glossary

Search the series glossary for Machine Learning, or browse all 2,575 terms.

Books that cover machine learning operations

Ultra Transcenders AI-300: Operationalizing Machine Learning and Generative AI Solutions

Know how models reach production, and stay there.

Coming soonStudy notes

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