skops is a Python library for sharing scikit-learn models and preparing them for production. Its skops.io component saves and loads Python-oriented model artifacts without pickle, with a workflow for reviewing unfamiliar types before deciding whether to trust them. It also includes skops.card for documenting a model’s behavior and intended use. The project describes itself as “a Python library helping you share your scikit-learn based models and put them in production.”
What skops adds to a scikit-learn workflow
Saving a trained estimator is only one part of deploying it. A team also needs a way to move the model into the environment that will use it, understand its dependencies, and communicate its intended use. skops addresses these needs with two distinct components:
skops.ioprovides a persistence workflow for scikit-learn estimators that avoids pickle and supports reviewing types in an artifact before loading.skops.cardprovides tooling for creating model cards that explain what a model does and how it should be used.
The model-card workflow can include storing cards as README.md files on the Hugging Face Hub. Hub hosting is one sharing option, not a requirement for using skops. See the skops project and its documentation for current details; the project documentation describes skops as under active development, so check the release documentation for current functionality and compatibility.
How skops.io reviews an artifact before loading
Pickle-based persistence formats can encode Python objects in ways that execute code during loading. The scikit-learn guide warns that loading pickle, joblib, or cloudpickle artifacts can execute arbitrary code, and recommends using them only when the source is trusted and verified.
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skops.io takes a different approach: it does not use pickle, and it loads only types and function references trusted by default or explicitly trusted by the user. Its API lets you inspect types that are not already trusted in a saved artifact. The review gives you information to make a trust decision; it does not certify that an artifact is safe or replace a broader security review. Consult the skops secure persistence guide for the current inspection and loading workflow.
A cautious loading workflow
- Establish provenance. Prefer artifacts from a known source and verify how they were produced and transferred.
- Inspect unfamiliar types. Use the skops.io inspection API to identify types that are not trusted by default before loading the artifact.
- Investigate before trusting. Check whether each unfamiliar type is expected for the model and its dependencies. Do not add types to a trust list merely to make loading succeed.
- Load and test in the intended environment. Confirm that the model behaves as expected and that the environment has the required compatible packages.
Should you use skops or ONNX?
The choice depends on what the serving system needs. ONNX is designed to run predictions without reconstructing the original Python object, which can suit a non-Python serving environment. skops.io keeps a Python-oriented object workflow. ONNX does not support every scikit-learn model, and custom estimators may take additional work to convert. The scikit-learn model persistence guide compares the formats and describes their tradeoffs.
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| Consideration | skops.io | ONNX | Pickle-based formats |
|---|---|---|---|
| What you need at inference time | Python-oriented workflow that retains the estimator object | Prediction without reconstructing the original Python object | Python object workflow |
| Model coverage | Suitable for supported Python estimators; confirm the current documentation for your model and dependencies | Does not cover every scikit-learn model; custom estimators may require additional work | Can persist Python objects, but loading has security and compatibility considerations |
| Trust handling | Inspect unfamiliar types and decide what to trust before loading | Different artifact and runtime model; verify the source and deployment requirements | Load only artifacts from a trusted and verified source because loading can execute arbitrary code |
| Serving environment | Requires a suitable Python environment and compatible dependencies | May run in an environment without Python | Requires Python and compatible dependencies |
| Performance | Depends on the model and workflow; measure for the target workload | Depends on the model and runtime; measure for the target workload | Depends on the model and workflow; measure for the target workload |
How to choose and deploy a persistence format
- Choose ONNX when the serving system needs predictions without the original Python estimator and your model can be converted with acceptable effort.
- Choose skops.io when you want a Python-oriented workflow and value inspecting unfamiliar artifact types before choosing whether to trust them.
- Use pickle, joblib, or cloudpickle only with verified trust. Their convenience does not remove the risk of code execution during loading.
- Check the target environment. Confirm the model format, Python or ONNX runtime, estimator support, and dependency versions where inference will run.
Do not assume a persisted scikit-learn model will load across different scikit-learn versions: the project’s persistence guidance says cross-version loading is unsupported. Preserve the training code, data references, and dependency versions, then test the artifact in the target environment before relying on it.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Document the model alongside sharing it
A serialized estimator does not explain its purpose or boundaries. With skops.card, teams can create a model card covering what the model does and how it should be used. Publishing a card as a README.md on the Hugging Face Hub can make that context available alongside a shared model, while leaving the choice of hosting and deployment environment up to the team.
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