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name: mlops description: "Use SageMaker Python SDK v3 MLOps workflows for Pipeline orchestration, workflow steps, model registry, monitoring, lineage, triggers, and feature store." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

SageMaker Python SDK v3 MLOps Sub-skill

Use this sub-skill for sagemaker-mlops workflow orchestration. It covers pipeline definition, step composition, registry flows, monitoring, lineage, triggers, feature-store-oriented orchestration, and local pipeline execution limits.

Trigger phrases

Read this sub-skill when the user says or implies:

• pipeline orchestration, workflow orchestration, or SageMaker Pipelines
• Pipeline, PipelineExecution, or PipelineGraph
• TrainingStep, ProcessingStep, TransformStep, or TuningStep
• ModelStep, ConditionStep, LambdaStep, CallbackStep, or FailStep
• QualityCheckStep, ClarifyCheckStep, or MonitorBatchTransformStep
• AutoMLStep, EMRStep, NotebookJobStep, or EMRServerlessStep
• PipelineSchedule, triggers, selective execution, retries, or cache config
• model registry, approval workflow, baseline registration, or pipeline gating
• feature store orchestration, FeatureGroupManager, or DatasetBuilder
• lineage, governance, sagemaker.core.lineage, or Lake Formation / Iceberg

Read this when

• You need to compose a production ML workflow that spans preprocessing, training, transform, registry, checks, or triggers.
• You need pipeline DAGs, retries, caching, selective execution, or execution inspection.
• You need feature-store orchestration, registry wiring, or governance hooks.
• You need lineage and compliance tracking around a workflow.

Do not read this when

• You only need low-level jobs or resources such as ProcessingJob, TrainingJob, TransformJob, Endpoint, or FeatureGroup CRUD; use ../core-resources/SKILL.md.
• You need to build training step_args; use ../training/SKILL.md.
• You need deployment or inference with ModelBuilder; use ../serving/SKILL.md.
• You need foundation-model customization or evaluation workflows; use ../model-customization/SKILL.md.

Non-negotiable v3 guardrails

• Use from sagemaker.mlops.workflow import ... or explicit workflow modules.
• Do not introduce legacy sagemaker.workflow.* imports unless you are writing a deprecated migration note and you also give the v3 replacement.
• Keep examples self-contained and placeholder-only.
• If a workflow needs PipelineSession, construct it from sagemaker.core.
• EMRServerlessStep exists in sagemaker.mlops.workflow.emr_serverless_step but is not re-exported from sagemaker.mlops.workflow.__all__ in this runtime build.
• Set AWS_DEFAULT_REGION or AWS_REGION before importing or constructing high-level workflow objects in a fresh environment.

Verified API surface

Use these verified imports and signatures from the inspection env as the source of truth:

• Pipeline(name='', parameters=None, pipeline_experiment_config=..., mlflow_config=None, steps=None, sagemaker_session=None, pipeline_definition_config=...)
• PipelineExecution(arn, sagemaker_session=Session())
• PipelineGraph(steps) and PipelineGraph.from_pipeline(pipeline)
• TrainingStep(name, step_args=None, display_name=None, description=None, cache_config=None, depends_on=None, retry_policies=None)
• ProcessingStep(...), TransformStep(...), and TuningStep(...)
• ModelStep(name, step_args, depends_on=None, retry_policies=None, display_name=None, description=None, repack_model_step_settings=None)
• ConditionStep(name, depends_on=None, display_name=None, description=None, conditions=None, if_steps=None, else_steps=None)
• QualityCheckStep(...), ClarifyCheckStep(...), AutoMLStep(...), EMRStep(...), NotebookJobStep(...), CallbackStep(...), LambdaStep(...), FailStep(...)
• MonitorBatchTransformStep(...)
• CacheConfig(enable_caching=False, expire_after=None)
• RetryPolicy(...), StepRetryPolicy(...), and SageMakerJobStepRetryPolicy(...)
• ParallelismConfiguration(max_parallel_execution_steps)
• SelectiveExecutionConfig(selected_steps, reference_latest_execution=True, source_pipeline_execution_arn=None)
• PipelineExperimentConfig(experiment_name, trial_name)
• PipelineDefinitionConfig(use_custom_job_prefix)
• PipelineSchedule(name=None, enabled=True, start_date=None, at=None, rate=None, cron=None)
• Trigger(name=None, enabled=True)
• CheckJobConfig(role, instance_count=1, instance_type='ml.m5.xlarge', volume_size_in_gb=30, volume_kms_key=None, output_kms_key=None, max_runtime_in_seconds=None, base_job_name=None, sagemaker_session=None, env=None, tags=None, network_config=None)

Short workflow

1. Classify the task: pipeline assembly or execution control, monitoring or governance, feature store or registry orchestration, or EMR / notebook / callback / Lambda routing.
2. Identify the upstream step args. Do not build them here:
   • processor.run(...) from ../core-resources/SKILL.md
   • model_trainer.train(...) from ../training/SKILL.md
   • transformer.transform(...) from ../core-resources/SKILL.md
   • model_builder.build() / register() from ../serving/SKILL.md
3. Import only the workflow classes needed for the branch you are building.
4. Create PipelineSession, parameters, property files, retry policies, cache config, and selective-execution settings as required.
5. Wire step dependencies by using upstream step_args and properties, especially ProcessingStep property files plus JsonGet for conditions.
6. Use Pipeline.create(), update(), upsert(), and start() in the correct order. PipelineExecution is for inspection after start.
7. For feature store, choose FeatureGroupManager when Lake Formation or Iceberg governance is part of the task; otherwise prefer the core FeatureGroup resource and helper functions.
8. For lineage, create Context / Action / Artifact / Association records with sagemaker.core.lineage, not the old shim.
9. Validate against local-mode limits before returning. If the workflow crosses into lower-level resources, training config construction, or deployment, hand off to the sibling sub-skill.

Reference map

• references/pipeline-workflows.md
• references/processing-feature-store-and-registry.md
• references/lineage-monitoring-and-governance.md
• references/troubleshooting.md

Handoff notes

• Keep this file router-like; put recipes, matrices, and failure recovery in the references.
• Do not link to the source checkout from runtime guidance.
• Use v3 replacements when the task mentions v2 pipeline, workflow, feature-store, or lineage APIs.
• Mention any unsupported local-mode or optional-dependency path explicitly.
• If a user asks for a direct FeatureGroup, TrainingJob, TransformJob, Endpoint, ModelBuilder, or ModelTrainer build path, route to the sibling sub-skill instead of expanding this one.