Machine learning frameworks are libraries or tools that simplify building, training, and deploying machine learning models. They provide pre-built functions, models, and utilities to handle data processing, model training, evaluation, and deployment.
Here are some of the most widely used machine learning frameworks:
| Framework | Best Known For | Notes |
|---|---|---|
| TensorFlow (Google) | Large-scale production ML | Heavyweight, supports mobile (TensorFlow Lite) |
| PyTorch (Meta/Facebook) | Research and prototyping | Very flexible, popular for deep learning |
| scikit-learn (Python) | Traditional ML (not deep learning) | Simple models: regression, trees, clustering |
| Keras (runs on TensorFlow) | Easy-to-use neural networks | High-level API to build deep learning models |
| XGBoost / LightGBM / CatBoost | Gradient boosting models | Tabular data problems, Kaggle competitions |
| ONNX (Microsoft) | Model exchange format | Convert models across frameworks |
| JAX (Google) | High-speed ML with NumPy syntax | Very fast (but early stage compared to PyTorch) |
| Apple Core ML | On-device ML for iOS/macOS | For converting and running models on Apple devices |
| MLX (Apple) | Experimental lightweight ML on Apple Silicon | Optimized for Mac GPUs, very new |