In this GCP-MPGC: ML Pipelines on Google Cloud course, you will learn about TensorFlow Extended (or TFX), which is Google’s production machine learning platform based on TensorFlow for management of ML pipelines and metadata. The first few modules discuss pipeline components, pipeline orchestration with TFX, how you can automate your pipeline through CI/CD, and how to manage ML metadata.

Then we will discuss how to automate and reuse ML pipelines across multiple ML frameworks such as tensorflow, pytorch, scikit learn, and xgboost. You will also learn how to use Cloud Composer to orchestrate your continuous training pipelines, and MLflow for managing the complete machine learning life cycle.

Skills Covered

  • Orchestrate model training and deployment with TFX and Cloud AI Platform. ?Operate deployed machine learning models effectively and efficiently. ?Perform continuous training using various frameworks (Scikit Learn, XGBoost, PyTorch) and orchestrate pipelines using Cloud Composer and MLFlow.
  • Integrate ML workflows with upstream and downstream data management workflows to maintain end-to-end lineage and metadata management.

Who Should Attend

This course is primarily intended for the following participants:

  • Data Scientists looking to deliver business impact by quickly converting from Machine Learning prototype to production.
  • Software Engineers looking to develop Machine Learning Engineering skills.
  • ML Engineers who want to adopt Google Cloud.

Course Curriculum


To get the most out of this course, participants should have:

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Course Modules

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