skops
A set of tools, related to machine learning in production.
Decision gist · record as of 2026-08-14
Yes, if you work with scikit-learn models in production or need to share them. The low-friction install, active maintenance, and pickle-free persistence address real security and reproducibility concerns. Verify the license before use in proprietary contexts, as it is currently marked unclear in the metadata.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.9 or later and scikit-learn as a runtime dependency.
- Low friction installation with five runtime dependencies (numpy, packaging, prettytable, scikit-learn, scipy).
- The project is actively maintained with recent commits and a stable release cadence.
License · maintenance · safety
(unclear) — License treatment is unclear—no SPDX identifier or raw license text is available in the package metadata. Verify the actual license before using in proprietary or restricted contexts.
last release 2026-04-20 (116 days) · last repo commit 2026-08-10 · 524 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 19,962,838 downloads/mo, #1,049 on PyPI
Alternatives
Verify before relying
pip install skops
import skops.io as sio
model_data = {'trained_model': 'example'}
sio.dump(model_data, 'model.skops')- Exact license identifier and terms—metadata shows 'unclear' treatment with no SPDX or raw license field populated.
- Whether model cards generated by skops.card are compatible with all Hugging Face Hub model metadata schemas.
- Performance overhead or compatibility constraints when persisting complex pipelines with custom transformers.
What it is and what it does
Skops is a Python library that solves the problem of safely sharing and deploying scikit-learn models in production. It provides two main tools: skops.io for secure, pickle-free serialization of estimators, and skops.card for generating model cards that document what a model does and how it should be used. The library is built on top of numpy, scipy, scikit-learn, and packaging, and targets developers and researchers who need to move trained models from development into production environments or share them on platforms like Hugging Face Hub.
The package is actively maintained, supports Python 3.9 through 3.14, and runs on macOS, Windows, and Unix-like systems. It is classified as Beta-stage software and sits in the top 5000 PyPI packages by download volume. The core value proposition is avoiding pickle's security risks while providing a standardized way to document and version models alongside their serialized artifacts.
Use it for
- Serialize a trained estimator to disk without pickle, then load and deploy it in a web service.
- Generate a model card documenting a model's performance, intended use, and limitations for sharing.
- Store multiple pipeline versions securely in a model registry without trusting pickle deserialization.
- Create reproducible model documentation that includes training data summaries and evaluation metrics.
- Share a trained model with collaborators in a format that is both human-readable and secure.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you work with scikit-learn models in production or need to share them.
The low-friction install, active maintenance, and pickle-free persistence address real security and reproducibility concerns. Verify the license before use in proprietary contexts, as it is currently marked unclear in the metadata.
Install
skops on PyPI
Before you install
Low friction installation with five runtime dependencies (numpy, packaging, prettytable, scikit-learn, scipy). The project is actively maintained with recent commits and a stable release cadence.
Requires Python 3.9 or later and scikit-learn as a runtime dependency.
License in practice
License treatment is unclear—no SPDX identifier or raw license text is available in the package metadata. Verify the actual license before using in proprietary or restricted contexts.
Quickstart
pip install skops
import skops.io as sio
model_data = {'trained_model': 'example'}
sio.dump(model_data, 'model.skops')Verify before relying
- Exact license identifier and terms—metadata shows 'unclear' treatment with no SPDX or raw license field populated.
- Whether model cards generated by skops.card are compatible with all Hugging Face Hub model metadata schemas.
- Performance overhead or compatibility constraints when persisting complex pipelines with custom transformers.
Package facts
| License | Not declared unclear |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesnumpypackagingprettytablescikit-learnscipy |
| Maintenance | Actively maintained 116 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 19,962,838 / month, #1,049 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI ApprovedOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: POSIXOperating System :: UnixProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: 3.9Programming Language :: Python :: Implementation :: CPythonTopic :: Scientific/EngineeringTopic :: Software Development |
Evidence: skops-0.14.0-py3-none-any.whl
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See also sklearndf · scikit-learn · scikit-learn-stubs · pypickle · sklearn-crfsuite · sklearn2pmml · scikeras · sagemaker-scikit-learn-extension · skfolio · scikit-learn-extra