Building ML Pipelines on Google Cloud

As machine learning models transition from experimental notebooks to production, manual deployments quickly become unsustainable. Automating these workflows with structured pipelines is the key to building reliable, repeatable, and scalable AI systems. This text-only course guides you through the foundational concepts of Machine Learning Operations (MLOps) and cloud-based pipeline architecture. You will gain the skills to conceptualize, write, and orchestrate robust ML pipelines that automate data preparation, training, and model deployment on Google Cloud. What you'll learn: Understand the core concepts of MLOps and the lifecycle of automated ML pipelines; Configure data ingestion, preprocessing, and model training steps as reusable pipeline components; Deploy and orchestrate workflows using Vertex AI Pipelines; Track pipeline metadata, artifacts, and execution history for full lineage visibility; Implement basic model monitoring and continuous integration principles for ML systems. Starting with fundamental pipeline terminology and cloud setup, this course guides you through step-by-step written explanations and practical code snippets. You will progress from simple local workflows to fully automated cloud deployments. This course is designed for aspiring ML engineers, data scientists, and cloud beginners who want to transition from static scripts to automated workflows. No prior cloud engineering experience is required. Start reading today to master the fundamentals of cloud-based ML pipelines.