GCP MLOps: Deploying AI and Machine Learning Models in Production

Many machine learning models never make it out of the experimental phase because moving them to production requires a completely different skill set. This course helps you overcome the deployment bottleneck by teaching you the fundamentals of MLOps on GCP. You will learn how to build robust deployment pipelines, ensuring that data scientists and engineers can collaborate to package, deploy, monitor, and update models reliably in a cloud environment. What you'll learn: - Understand foundational MLOps concepts, lifecycle phases, and key terminology - Package and deploy machine learning models to GCP prediction services - Configure automated pipelines for continuous integration and delivery of ML workflows - Monitor production models to detect performance degradation and data drift - Establish collaborative workflows between development and operations using model registries This course begins with core definitions and cloud architecture fundamentals before guiding you through step-by-step configurations for model packaging, deployment strategies, and continuous monitoring. It is designed for beginners, aspiring data engineers, and data scientists looking to expand into production operations, with no advanced DevOps experience required. Start building dependable, production-ready machine learning pipelines today.