Managing Features in Vertex AI: An MLOps Foundation

In modern machine learning, managing data features consistently across training and serving is one of the biggest challenges to scaling models. This written course guides you through the foundational concepts of MLOps using Vertex AI, focusing on how to organize, store, and serve features efficiently. You will transition from manual data preparation to automated, centralized feature management. By understanding how to leverage a feature store, you will ensure your machine learning pipelines remain consistent, reproducible, and ready for production. What you'll learn: Understand foundational MLOps concepts and the role of feature stores in machine learning lifecycles; Configure Vertex AI Feature Store to ingest, store, and share features across different teams; Serve features in both batch mode for training and low-latency online mode for real-time predictions; Monitor feature drift and data quality to maintain high model performance over time; Apply modern MLOps best practices, including integrating feature stores with vector databases for retrieval-augmented generation (RAG) patterns. This course starts with key terminology and foundational definitions of feature engineering and MLOps before moving into step-by-step written explanations of Vertex AI configurations. You will progress from basic data ingestion to advanced monitoring strategies and modern architectural patterns. This text-only course is designed for aspiring ML engineers, data scientists, and developers who are new to MLOps. No prior experience with Vertex AI is required, though a basic understanding of machine learning concepts is helpful. Start reading today to master feature management and elevate your machine learning operations.