GCP Production Machine Learning Systems

Moving a machine learning model from a local notebook to a reliable production environment requires a distinct set of engineering skills. This text-based course guides you through the essential architectural patterns and tools needed to build, deploy, and monitor scalable ML pipelines on GCP. You will transition from training isolated models to building fully automated, production-ready ML systems. By reading our detailed explanations and analyzing structured code examples, you will understand how to manage data pipelines, orchestrate workflows, and maintain model performance over time. What you'll learn: 1. Understand foundational MLOps concepts and production system architecture. 2. Build automated ML pipelines to orchestrate data ingestion, training, and deployment. 3. Configure model serving components for low-latency and high-throughput predictions. 4. Implement continuous monitoring to detect data drift and model performance degradation. 5. Apply modern CI/CD practices specifically tailored for machine learning workflows. 6. Explore modern vector databases and retrieval-augmented generation patterns in production. The course begins with essential terminology and the core architecture of production systems before moving into pipeline configuration and monitoring strategies. It concludes with practical guidance on maintaining system health and scaling your infrastructure. This course is designed for aspiring ML engineers, data scientists, and software developers who want to transition into production-level engineering, with no prior advanced cloud experience required. Start building reliable, automated machine learning systems on the cloud today.