Machine Learning Operations (MLOps) on Google Cloud

Coursera MOOC / Non-credit USD 49
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Machine Learning Operations (MLOps) on Google Cloud

About this course

This learning path is designed for data scientists and ML engineers looking to bridge the gap between machine learning prototypes and production-ready systems on Google Cloud. Learners will explore the full MLOps lifecycle, including feature management with Vertex AI Feature Store, robust model evaluation for predictive and generative AI, and the orchestration of automated workflows. The path concludes with advanced training on building production-grade pipelines using the Kubeflow SDK, Google Cloud components, and AI-driven development with the Data Science Agent.

What you'll learn

  • Understand the MLOps lifecycle
  • Manage features with Vertex AI Feature Store
  • Evaluate machine learning models for both predictive and generative AI
  • Orchestrate automated workflows
  • Build production-grade ML pipelines using Kubeflow SDK and Google Cloud components

Course objectives

  • Bridge the gap between machine learning prototypes and production systems
  • Implement robust model evaluation strategies
  • Facilitate automated workflows for machine learning operations

Skills you'll gain

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