MLOps on GCP is a practical, end-to-end guide designed for ML Engineers, MLOps Engineers, and ML Platform Engineers who want to build, deploy, and operate machine learning systems at scale using Google Cloud Platform. The book focuses on transforming machine learning models from experimental prototypes into reliable, production-grade systems by leveraging core GCP services such as Vertex AI, BigQuery, Cloud Storage, and Cloud Build. It provides a structured approach to designing scalable, cloud-native ML platforms while bridging the gap between data science experimentation and operational excellence.
Through real-world scenarios and hands-on architectural patterns, readers will learn how to implement automated ML pipelines, enable CI/CD for machine learning workflows, and manage data, model, and experiment versioning effectively. The book also explores deployment strategies for batch and real-time inference, along with best practices for monitoring, observability, and drift detection using GCP-native tools. Additionally, it highlights how to integrate advanced AI capabilities, including generative AI and foundation models within the Vertex AI ecosystem, to build next-generation intelligent applications.
By the end of this book, readers will have a strong foundation to design and operate production-ready ML systems on GCP. Whether you are modernizing existing ML workflows or building new AI platforms from scratch, this guide equips you with the essential tools, patterns, and strategies needed to deliver scalable, efficient, and business-driven machine learning solutions in the Google Cloud ecosystem.
