Building Machine Learning Pipelines on Google Cloud

Transitioning machine learning models from local notebooks to production requires robust, automated workflows. This course guides you through the foundational concepts of designing, executing, and managing machine learning pipelines on Google Cloud. You will understand how to transition from manual model training to automated, repeatable processes. Through detailed written explanations and structured code walk-throughs, you will gain the practical skills needed to structure data ingestion, model training, and deployment using industry-standard cloud tools. What you'll learn: Understand core machine learning pipeline concepts and foundational terminology; Build automated training workflows using TensorFlow and cloud orchestration tools; Implement metadata tracking and lineage to monitor pipeline runs; Configure data ingestion and preprocessing stages for scalable training; Deploy trained models to cloud endpoints for reliable serving; Apply basic MLOps practices to maintain and update production pipelines. The course starts with essential definitions and pipeline architecture before guiding you through step-by-step code implementations for each pipeline stage. You will study practical examples of data preparation, model training orchestration, and automated deployment. This course is designed for aspiring ML engineers, data scientists, and developers new to cloud-based workflows, with no prior cloud engineering experience required. Start reading today to build your first production-ready machine learning pipeline on the cloud.