PMLE - Collaborating Within and Across Teams to Manage Data and Models - Section 2.2

Prototype models in notebooks using Agent Platform Workbench and Colab Enterprise, applying collaboration and security best practices with common frameworks such as PyTorch, scikit-learn, and JAX, and using Model Garden models.

Prototype models in Agent Platform Workbench and Colab Enterprise using frameworks such as PyTorch, scikit-learn, and JAX, applying collaboration and access-control best practices suited to shared notebook environments. Recognise when to call a Model Garden foundation model from a notebook versus training a custom model from scratch.

Agent Platform WorkbenchColab EnterprisePyTorch and JAXModel Garden

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Explore and preprocess data for ML across tabular, text, and image types, choosing the right tool for scale such as BigQuery, Dataflow, Apache Spark, and in-memory Python frameworks, consolidating features in the Agent Platform Feature Store, and protecting personally identifiable information.Section 2.1
Track and run ML experiments, choosing the right environment among Experiments on the Agent Platform, Agent Platform Pipelines, and Kubeflow Pipelines, evaluating predictive and gen AI solutions including LLM-as-a-judge, and tracking artifacts, versions, and lineage with ML Metadata.Section 2.3

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