Amazon SageMaker Pipelines
AWS managed service for CI/CD-capable ML pipelines with integrated experiment tracking, model registry, and deployment automation.
SageMaker Pipelines offers AWS-native ML pipeline orchestration with integrated model registry, experiments, and deployment automation.
Explanation
SageMaker Pipelines orchestrates ML workflows as DAGs with steps for processing, training, evaluation, registration, and deployment. It integrates SageMaker Experiments, Model Registry, and automatic model approvals.
Marketing Relevance
SageMaker Pipelines is the native MLOps solution for AWS-centric organizations.
Common Pitfalls
AWS vendor lock-in. Costs can escalate quickly with many pipelines. Debugging limited in managed environments.
Origin & History
AWS launched SageMaker in 2017 as a managed ML service. SageMaker Pipelines was introduced at re:Invent 2020. Since then, Model Dashboard, Shadow Testing, and MLflow integration have been added. SageMaker is the most widely used cloud ML platform.
Comparisons & Differences
Amazon SageMaker Pipelines vs. Vertex AI Pipelines
Vertex AI Pipelines uses Kubeflow Pipelines SDK; SageMaker Pipelines has its own SDK with deeper AWS integration.
Amazon SageMaker Pipelines vs. Apache Airflow
Airflow is a general workflow orchestrator; SageMaker Pipelines is ML-specific with native training and serving.