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