ML Pipelines with SageMaker and CI/CD
Explore how to construct, automate, and govern machine learning pipelines using Amazon SageMaker. Learn to build repeatable workflows with DAGs, trigger pipelines automatically through CI/CD and events, and use the Model Registry to control model promotion and deployment. This lesson helps you implement scalable and auditable MLOps pipelines for production-ready machine learning systems.
Operationalizing ML demands more than model accuracy. Production systems require repeatability, meaning that every training run must be reproducible from the same code and data. They require auditability because regulators and stakeholders need to trace a deployed model back to its training data and evaluation metrics. They also require scale, enabling teams to run dozens of experiments weekly without manual orchestration bottlenecks.
SageMaker Pipelines is AWS's native orchestration service built for these requirements. It models ML workflows as directed acyclic graphs (DAGs), where each step (data processing, training, evaluation, and registration) executes as a managed job with tracked inputs and outputs. Pipelines integrate directly with CI/CD systems: AWS CodePipeline triggers execution on code commits, Amazon EventBridge fires on data arrival events, and the SageMaker Model Registry serves as the promotion handoff mechanism between training and deployment environments. In this lesson, we will discuss three capabilities:
Constructing pipeline DAGs with condition-based gating.
Triggering pipelines automatically from external events.
Using the Model Registry to govern model promotion across dev, staging, and production.
Understanding SageMaker Pipelines as DAGs
A DAG is a structure where nodes represent computational steps, and directed edges represent dependencies, with no cycles allowed. ML workflows are natural DAGs: preprocessing must complete before training, training before evaluation, and evaluation before registration. No step feeds back into a prior step within a single execution. SageMaker Pipelines leverages this structure to determine execution order, parallelize independent steps, and retry failed nodes without rerunning the entire workflow.
Core step types and execution flow
SageMaker Pipelines provides purpose-built step types that map directly to ML lifecycle stages:
ProcessingStep: Wraps a SageMaker Processing job for data preprocessing and feature engineering. Triggers on pipeline execution start and produces cleaned datasets stored as S3 artifacts.
TrainingStep: Configures a SageMaker Training job with an Estimator, hyperparameters, and ...