Workflow Orchestration
Explore how to automate and orchestrate machine learning workflows on AWS by chaining data processing, training, evaluation, and deployment steps. Understand the strengths and use cases of SageMaker Pipelines, AWS Step Functions, and Apache Airflow on Amazon MWAA to select the right orchestration tool for your ML lifecycle. This lesson helps you design scalable, automated pipelines that improve reproducibility and operational efficiency in ML projects.
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Once models are safely deployed through strategies like shadow variants, blue/green deployments, and canary rollouts, the next challenge is automating the entire ML life cycle that produces those deployments. Manually executing data processing, training, evaluation, and deployment steps introduces human error, hurts reproducibility, and does not scale across teams or projects. Workflow orchestration solves this by chaining these steps into automated, repeatable pipelines in which each stage triggers the next based on defined conditions and data dependencies.
This lesson covers three AWS-relevant orchestration tools you need to know for the AWS Certified Machine Learning Engineer – Associate exam. SageMaker Pipelines is the ML-native default. AWS Step Functions handles broader, multi-service coordination. Apache Airflow, managed through Amazon MWAA, supports hybrid and multicloud scenarios. The exam consistently tests your ability to select the right orchestrator based on project requirements, so understanding the trade-offs between these three tools is essential.
Regardless of which orchestrator you choose, the key operational metrics remain consistent: pipeline execution time, step failure rates, resource utilization per step, and model accuracy at the evaluation gate. These metrics feed into CloudWatch dashboards and alarms, closing the loop between pipeline automation and operational monitoring.
SageMaker Pipelines fundamentals
SageMaker Pipelines is a fully managed, ML-specific orchestration service that is tightly integrated with the SageMaker ecosystem. It eliminates infrastructure management and provides strong support for the ML life cycle stages that matter most: data processing, training, evaluation, model registration, and deployment.
Core abstractions
The service is built around three core abstractions that map directly to how ML engineers think about workflows.
Pipeline: The top-level object that defines a directed acyclic graph (DAG) of steps, ...