mleap
MLeap Python API
Decision gist · record as of 2026-08-14
Yes. MLeap is actively maintained, carries no vulnerabilities, and solves a real portability problem for machine learning pipelines. The low install friction and permissive license make it a straightforward addition. Install if you need to serialize scikit-learn pipelines or integrate with Bundle.ML-based systems; skip if you have no need for model interchange or deployment outside your current framework.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Low friction: pure Python wheel with five standard dependencies (numpy, six, scipy, pandas, scikit-learn).
- Actively maintained with a release 24 days old and no known vulnerabilities.
License · maintenance · safety
permissive license (permissive) — Permissive Apache license; no restrictions on commercial or proprietary use.
last release 2026-07-21 (24 days) · last repo commit 2026-07-21 · 1,540 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 2,313,539 downloads/mo, #3,146 on PyPI
Alternatives
Verify before relying
pip install mleap
from mleap.sklearn.pipeline import to_bundle
import pandas as pd
df = pd.DataFrame([[0.1, 0.2]], columns=['f1', 'f2'])
to_bundle(fitted_pipeline, 'jar:file:/tmp/model.zip', df)- Whether deserialization from Bundle.ML to scikit-learn is fully implemented (description indicates it is 'coming soon').
- Whether TensorFlow integration mentioned as 'coming soon' is now available in version 0.25.2.
- Performance characteristics and bundle file size overhead compared to native formats.
- Exact API surface and whether all scikit-learn transformer types are supported.
What it is and what it does
MLeap is a Python library that provides serialization and deserialization of machine learning pipelines to a portable Bundle.ML format. It integrates with scikit-learn and enables pipelines built with numpy, scipy, pandas, and scikit-learn to be exported to a common interchange format. The library maintains mathematical parity across implementations, allowing models to produce consistent results when moved between different execution environments.
The package depends on numpy, six, scipy, pandas, and scikit-learn, with optional gensim integration for Word2Vec serialization. It is actively maintained, supports Python 3.10 through 3.13, and carries no known security vulnerabilities. Scikit-Learn deserialization is noted as in development.
Use it for
- Export a fitted scikit-learn pipeline to Bundle.ML for deployment in a lightweight runtime environment.
- Serialize a gensim Word2Vec model to Bundle.ML for consistent text vectorization across environments.
- Share trained ML pipelines between teams via a standardized interchange format.
- Build reproducible ML workflows where serialized models execute consistently across different contexts.
- Integrate scikit-learn models with systems that consume Bundle.ML format.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
MLeap is actively maintained, carries no vulnerabilities, and solves a real portability problem for machine learning pipelines. The low install friction and permissive license make it a straightforward addition. Install if you need to serialize scikit-learn pipelines or integrate with Bundle.ML-based systems; skip if you have no need for model interchange or deployment outside your current framework.
Install
mleap on PyPI
Before you install
Low friction: pure Python wheel with five standard dependencies (numpy, six, scipy, pandas, scikit-learn). Actively maintained with a release 24 days old and no known vulnerabilities.
Requires Python 3.10 or later.
License in practice
Permissive Apache license; no restrictions on commercial or proprietary use.
Quickstart
pip install mleap
from mleap.sklearn.pipeline import to_bundle
import pandas as pd
df = pd.DataFrame([[0.1, 0.2]], columns=['f1', 'f2'])
to_bundle(fitted_pipeline, 'jar:file:/tmp/model.zip', df)Verify before relying
- Whether deserialization from Bundle.ML to scikit-learn is fully implemented (description indicates it is 'coming soon').
- Whether TensorFlow integration mentioned as 'coming soon' is now available in version 0.25.2.
- Performance characteristics and bundle file size overhead compared to native formats.
- Exact API surface and whether all scikit-learn transformer types are supported.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesnumpysixscipypandasscikit-learn |
| Maintenance | Actively maintained 24 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 2,313,539 / month, #3,146 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersLicense :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Topic :: Communications :: ChatTopic :: InternetTopic :: Software Development :: Libraries :: Python Modules |
Evidence: mleap-0.25.2-py3-none-any.whl
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