{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/3"}],"enrichment":{"capability":"Provides high-level orchestration for Amazon SageMaker workflows, including pipeline definitions, step implementations, and model building utilities that coordinate training, serving, and core SageMaker components.","skillfed_tags":["aws-sagemaker","ml-orchestration","workflow-automation"],"use_cases":["Define multi-step ML pipelines with conditional logic, parallel execution, and retry policies for SageMaker workflows","Orchestrate training, model evaluation, bias checking, and deployment steps in a single declarative pipeline","Integrate Lambda functions, EMR clusters, and batch transform jobs into SageMaker workflow definitions","Configure selective execution and parallelism settings for large-scale model training and serving workflows","Build and register models with quality and bias checks as part of an automated MLOps pipeline"],"what_it_does":"sagemaker-mlops is the orchestration layer of the SageMaker SDK, sitting above the Core, Train, and Serve packages. It provides pipeline definitions, step implementations (AutoML, model creation, bias checks, EMR, Lambda, batch transform monitoring, and others), and configuration utilities for parallel execution, retry policies, and selective execution. The package resolves architectural constraints by centralizing workflow logic that needs to import from multiple lower-level SageMaker components.\n\nYou use it to define and execute multi-step machine learning workflows on Amazon SageMaker. It exposes classes for pipelines, steps, and specialized step types, along with configuration objects for controlling execution behavior. The package also re-exports ModelBuilder from sagemaker-serve for convenience. It requires Python 3.10 or later and depends on boto3, botocore, cryptography, pyiceberg, pyarrow, and s3fs alongside the three core SageMaker packages.","worth_installing":"Yes, if you are building machine learning workflows on Amazon SageMaker. The package is actively maintained, has low install friction, carries a permissive Apache 2.0 license, and integrates cleanly with the modular SageMaker SDK architecture. The Alpha status suggests it is still evolving; verify stability requirements for your use case before production deployment."},"id":"sagemaker-mlops","links":{"html":"https://skillfed.io/packages/sagemaker-mlops","md":"https://skillfed.io/packages/sagemaker-mlops.md","pypi":"https://pypi.org/project/sagemaker-mlops/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-11","license_spdx":null,"license_treatment":"permissive","name":"sagemaker-mlops","python_support":"supports_current","summary":"SageMaker MLOps package for workflow orchestration and model building"},"popularity":{"monthly_downloads":1305686,"position":4080,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"1.19.0"}