SageMaker Pipelines

SageMaker Pipelines is a fully managed service within AWS SageMaker that enables developers and data scientists to build, automate, and manage end-to-end machine learning workflows. It provides a native orchestration tool for creating, executing, and monitoring ML pipelines, integrating steps like data preprocessing, model training, evaluation, and deployment. This service helps standardize ML processes, improve reproducibility, and streamline collaboration across teams.

Also known as: AWS SageMaker Pipelines, Amazon SageMaker Pipelines, SageMaker ML Pipelines, SageMaker workflow, SMP

Why learn SageMaker Pipelines?

Developers should use SageMaker Pipelines when building production-grade ML systems on AWS, as it automates complex workflows, reduces manual errors, and ensures consistency in model development and deployment. It is particularly valuable for scenarios requiring frequent retraining, A/B testing, or compliance with regulatory standards, such as in finance, healthcare, or e-commerce applications. Learning this skill is essential for roles focused on MLOps, data engineering, or cloud-based AI solutions.

Learning Resources

• docs: AWS SageMaker Pipelines Documentation
• tutorial: Build ML workflows with SageMaker Pipelines - AWS Tutorial
• course: SageMaker Pipelines Course on Coursera
• video: Introduction to SageMaker Pipelines - YouTube Video
• book: Machine Learning Engineering on AWS Book