CI/CD Pipelines for ML

• Treat a retrained model like code: ship it through a CI/CD pipeline
• CodePipeline conducts, CodeBuild runs the work, CodeDeploy pushes the artifact
• Use SageMaker Pipelines as the ML-native DAG for the model build
• Pick Step Functions when the workflow spans many AWS services
• Choose Amazon MWAA when the team already runs Apache Airflow
• Model Registry approval is the gate between training and deployment
• Let EventBridge turn a registry approval into a deploy-pipeline trigger
• Update a live endpoint with a deployment guardrail, not a raw swap
• Match the traffic-shifting mode to the risk: all-at-once, canary, or linear
• A CloudWatch alarm during the baking period auto-rolls-back the deploy
• Use a rolling deployment when a full second fleet is too much to provision
• Stand up a standardized MLOps pipeline with a SageMaker Projects template
• Drive automatic retraining with an EventBridge rule
• Code repositories and pipelines are complementary, not the same thing
• Gate model promotion on metrics with a ConditionStep, not just unit tests
• Nest the tools: CodePipeline runs the release, SageMaker Pipelines runs the model build
• Cache unchanged SageMaker Pipeline steps with CacheConfig and an ISO-8601 expire_after
• EventBridge needs a role with sagemaker:StartPipelineExecution and an events.amazonaws.com trust to launch a pipeline
• Pass event fields into a pipeline as parameters with SageMakerPipelineParameters JSONPath
• Match commits to a branch with a CodeCommit Repository State Change event pattern
• Notify or chain on job outcomes with SageMaker Job State Change events
• Append .sync to a SageMaker Task ARN to make Step Functions wait for the job
• Combine Retry and Catch in Step Functions for backoff plus fallback routing
• A Lambda that starts a SageMaker job also needs iam:PassRole for the job's execution role

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