sagemaker-mlops
SageMaker MLOps package for workflow orchestration and model building
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Requires AWS credentials and SageMaker permissions to execute pipelines.
- The 9 runtime dependencies (sagemaker-core, sagemaker-train, sagemaker-serve, boto3, botocore, cryptography, pyiceberg, pyarrow, s3fs) must be available.
License · maintenance · safety
permissive license (permissive) — Apache License 2.0 (permissive). You may use, modify, and distribute this package freely in commercial and open-source projects, provided you include the license text and note any modifications you make.
last release 2026-08-11 (3 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,305,686 downloads/mo, #4,080 on PyPI
Alternatives
Verify before relying
pip install sagemaker-mlops
from sagemaker.mlops import Pipeline
pipeline = Pipeline(name="my-pipeline", steps=[])
pipeline.upsert()
pipeline.start()- Whether all 9 runtime dependencies are automatically resolved or must be installed separately
- Whether this package is intended for development/editable install or production use
- Stability guarantees given the 'Alpha' development status classifier
- Concrete usage examples for specific step types beyond what the fact sheet documents
What it is and 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.
You 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.
Use it for
- 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
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
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.
Install
sagemaker-mlops on PyPI
Before you install
Low install friction; pure Python wheel with no compiled dependencies. Actively maintained as of 3 days ago. Depends on 9 runtime packages including sagemaker-core, sagemaker-train, and sagemaker-serve, which form the SageMaker SDK's modular architecture.
Requires Python 3.10 or later. Requires AWS credentials and SageMaker permissions to execute pipelines. The 9 runtime dependencies (sagemaker-core, sagemaker-train, sagemaker-serve, boto3, botocore, cryptography, pyiceberg, pyarrow, s3fs) must be available.
License in practice
Apache License 2.0 (permissive). You may use, modify, and distribute this package freely in commercial and open-source projects, provided you include the license text and note any modifications you make.
Quickstart
pip install sagemaker-mlops
from sagemaker.mlops import Pipeline
pipeline = Pipeline(name="my-pipeline", steps=[])
pipeline.upsert()
pipeline.start()Verify before relying
- Whether all 9 runtime dependencies are automatically resolved or must be installed separately
- Whether this package is intended for development/editable install or production use
- Stability guarantees given the 'Alpha' development status classifier
- Concrete usage examples for specific step types beyond what the fact sheet documents
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 9 packagessagemaker-coresagemaker-trainsagemaker-servecryptographyboto3botocorepyicebergpyarrows3fs |
| Maintenance | Actively maintained 3 days since the last release |
| First released | |
| Downloads | 1,305,686 / month, #4,080 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaIntended Audience :: DevelopersLicense :: OSI Approved :: Apache Software LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12 |
Evidence: sagemaker_mlops-1.19.0-py3-none-any.whl
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See also sagemaker · weasel · sagemaker-serve · stepfunctions · outerbounds · sagemaker-core · sagemaker-train · zenml · azureml-pipeline-steps · sagemaker-experiments