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MLOps & tracking

Connect operational practices, experiment tracking, and shared metadata to preserve the code, data, and results behind every model.

4 guides · Suggested reading order

Start here

What is MLOps?

Understand operations across the model development lifecycle.

Read the guide

Continue the learning path

  1. MLOps core principles

    Connect collaboration, automation, version control, and monitoring.

  2. Experiment tracking in machine learning

    Record and compare parameters, metrics, and outputs.

  3. What is a metadata store for machine learning

    Preserve the context and lineage behind your results.

Go deeper

Put the foundations into practice

Explore practical guides and resources to take the next step.