ML Workflow Orchestration with Vertex AI Pipelines

Scaling machine learning from a local notebook to a reliable, repeatable production pipeline is one of the biggest challenges in modern AI. This text-based course guides you through the process of automating and managing your ML workflows using Vertex AI Pipelines. You will transition from manual model training to fully orchestrated MLOps pipelines, discovering how to connect data ingestion, preprocessing, training, and deployment into a cohesive, automated system that ensures reproducibility and tracking. What you'll learn: - Understand foundational MLOps concepts and the core business drivers behind pipeline orchestration. - Build reusable pipeline components using modern Python-based SDKs. - Track pipeline metrics, parameters, and artifact lineage for complete model auditability. - Configure automated triggers and integrate CI/CD practices into your machine learning workflows. - Deploy trained models to prediction endpoints as part of a seamless pipeline. This course begins with essential terminology, basic concepts, and foundational definitions of pipeline architecture before moving into step-by-step written guides and configuration examples. You will work through practical scenarios, reading through clear explanations of pipeline definitions and practicing with structured written exercises. This course is designed for data scientists, ML engineers, and developers who are new to pipeline orchestration. No prior experience with cloud-based MLOps is required, making it the perfect starting point for beginners. Start reading today to bring automation and reliability to your machine learning workflows.