PMLE - Automating and Orchestrating ML Pipelines - Section 5.2

Automate model retraining by determining an appropriate retraining policy and deploying models in continuous integration, continuous delivery, and continuous training pipelines using services such as Cloud Build.

Determine an appropriate retraining policy by weighing data drift frequency, model performance thresholds, and cost, then automate retraining within a continuous training pipeline. Configure CI/CD/CT pipelines using Cloud Build to trigger, test, and promote new model versions without manual intervention.

Retraining policyCI/CD/CT pipelinesCloud Build

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Develop end-to-end ML pipelines that validate data and models, orchestrate managed and unmanaged services from templates or custom solutions such as Agent Platform Pipelines, Managed Service for Apache Airflow, and Ray on the Agent Platform, and ensure consistent data preprocessing between training and serving.Section 5.1

Back to all Automating and Orchestrating ML Pipelines objectives, or the PMLE cert hub.

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