Orchestrating and Evaluating Machine Learning Pipelines

Moving machine learning models from a local notebook to a reliable production environment requires structured, automated workflows. This text-based course guides you through the foundational concepts of orchestrating, analyzing, and evaluating machine learning pipelines. You will transition from manual model training to designing automated pipelines that ingest data, train models, and evaluate performance consistently, learning how to detect issues early and maintain model quality over time. What you'll learn: Understand foundational machine learning pipeline concepts and architecture; Orchestrate automated workflows to ingest, preprocess, and train ML models; Evaluate model performance using key metrics and validation strategies; Analyze data and model drift to maintain accuracy in changing environments; Implement basic monitoring and logging for production pipeline reliability. The course begins with core definitions and structural concepts before guiding you through pipeline design, evaluation techniques, and modern monitoring strategies. You will study practical, text-based examples and conceptual walkthroughs to build a solid operational foundation. This course is designed for aspiring ML engineers, data scientists, and developers new to pipeline orchestration, requiring only basic programming familiarity. Start building robust, automated machine learning workflows today.