AI / ML ML Framework mature
scikit-learn
Simple and efficient tools for classical ML in Python
62.0K stars
2.8K contributors
Since 2007
Python library for classical machine learning algorithms including classification, regression, clustering, dimensionality reduction with consistent API and excellent documentation.
License
BSD-3-Clause
Min RAM
512 MB
Min CPUs
1 core
Scaling
single_node
Complexity
beginner
Performance
medium
Self-hostable
✓
K8s native
✕
Offline
✓
Pricing
fully free
Docs quality
excellent
Vendor lock-in
none
Use cases
- ✓ Customer churn prediction
- ✓ Fraud detection with ensemble methods
- ✓ Text classification with TF-IDF + SVM
- ✓ A/B test analysis and statistical modeling
- ✓ Feature engineering pipelines
Anti-patterns / when NOT to use
- ✕ Not for deep learning or neural networks
- ✕ Single-machine only - no distributed training
- ✕ Not for GPU-accelerated workloads
- ✕ Not for real-time streaming predictions
Integrates with
Complements
Replaces / alternatives to
Technical specs
Language
PythonC
API type
SDK
Protocols
HTTP
Deployment
pip
SDKs
python
Community
GitHub stars 62.0K
Contributors 2.8K
Commit frequency daily
Plugin ecosystem medium
Backing Community / Inria
Funding foundation
Release
Latest version
1.6 Last release 2025-12
Since 2007
Best fit
Team size
solosmallmedium
Industries
generalfintechhealthcareresearchmarketing