sagemaker-scikit-learn-extension
Open source library extension of scikit-learn for Amazon SageMaker.
Decision gist · record as of 2026-08-14
No, unless you are maintaining an existing SageMaker Autopilot workflow. The package is dormant (last release 2022-02-18), has high install friction (requires conda for mlio version 0.7), and is tested only on Python 3.7. Compatibility with modern Python and scikit-learn versions is unverified. For new projects, use scikit-learn directly or actively-maintained alternatives.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- mlio version 0.7 is only available via conda, not pip.
- Package is tested on Python 3.7; compatibility with newer versions is unspecified.
- High install friction: the package is distributed as a source tarball and requires conda to install an optional but commonly-needed dependency (mlio version 0.7).
License · maintenance · safety
Apache License 2.0 (permissive) — Licensed under Apache License 2.0 (permissive). You may use, modify, and distribute this package freely in commercial and private projects, provided you include a copy of the license.
last release 2022-02-18 (1638 days) · last repo commit 2024-02-01 · 41 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 302,468 downloads/mo, #7,822 on PyPI
Alternatives
Verify before relying
# Install from pip
pip install sagemaker-scikit-learn-extension
# For I/O functionality, also install mlio via conda
conda install -c mlio -c conda-forge mlio-py==0.7
# Example usage with imputation and preprocessing tools
pip install --upgrade .[test]- Whether the package works reliably on Python versions newer than 3.7.
- Current compatibility with recent scikit-learn versions, given dormant maintenance since 2022-02-18.
- Whether all submodules install without additional manual steps beyond the documented conda requirement.
What it is and what it does
SageMaker Scikit-Learn Extension is a collection of scikit-learn-compatible estimators and transformers built to fill gaps in scikit-learn's standard library and support AWS SageMaker's Autopilot automated machine learning service. It provides specialized tools for dimension reduction, feature extraction from datetime and time-series data, text vectorization, missing-value imputation with custom masking logic, and categorical encoding schemes. The package is designed as a repository for estimators that don't meet scikit-learn's strict inclusion criteria but are useful in production ML pipelines.
The project is dormant—the last release was 2022-02-18, and it has not been updated since then. It is tested on Python 3.7 only. The package integrates with SageMaker's training containers and includes utilities for feature and target transformation, making it a legacy tool primarily suited for maintaining existing SageMaker workflows rather than new projects.
Use it for
- Preprocess tabular data for SageMaker Autopilot by applying imputation and categorical encoding transformers.
- Extract numeric features from datetime columns for time-aware machine learning models.
- Handle extreme values and heavy-tailed distributions with specialized transformers.
- Encode categorical features using supervised or similarity-based encoding methods.
- Perform dimension reduction on sparse matrices without dense conversion.
- Extract features from time-series sequence data using provided extractors.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
No, unless you are maintaining an existing SageMaker Autopilot workflow.
The package is dormant (last release 2022-02-18), has high install friction (requires conda for mlio version 0.7), and is tested only on Python 3.7. Compatibility with modern Python and scikit-learn versions is unverified. For new projects, use scikit-learn directly or actively-maintained alternatives.
Install
sagemaker-scikit-learn-extension on PyPI
Before you install
High install friction: the package is distributed as a source tarball and requires conda to install an optional but commonly-needed dependency (mlio version 0.7). Maintenance is dormant—last release was 2022-02-18, with no updates since then.
mlio version 0.7 is only available via conda, not pip. Package is tested on Python 3.7; compatibility with newer versions is unspecified.
License in practice
Licensed under Apache License 2.0 (permissive). You may use, modify, and distribute this package freely in commercial and private projects, provided you include a copy of the license.
Quickstart
# Install from pip
pip install sagemaker-scikit-learn-extension
# For I/O functionality, also install mlio via conda
conda install -c mlio -c conda-forge mlio-py==0.7
# Example usage with imputation and preprocessing tools
pip install --upgrade .[test]Verify before relying
- Whether the package works reliably on Python versions newer than 3.7.
- Current compatibility with recent scikit-learn versions, given dormant maintenance since 2022-02-18.
- Whether all submodules install without additional manual steps beyond the documented conda requirement.
Package facts
| License | Apache License 2.0 permissive |
| Python support | Not specified |
| Install friction | High. Source build required |
| Runtime dependencies | None |
| Maintenance | Dormant 1,638 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 302,468 / month, #7,822 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaLicense :: OSI Approved :: Apache Software License |
Evidence: sagemaker-scikit-learn-extension-2.5.0.tar.gz
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See also sagemaker-train · feature-engine · skrub · scikit-learn-extra · sagemaker-data-insights · missingpy · category-encoders · sagemaker-inference · sagemaker · sklearndf