1. PyTorch A deep learning framework preferred by researchers and applied scientists.
2. TensorFlow Makes it easy to create ML models that can run in any environment.
3. Hugging Face Transformers An open-source Python library providing APIs and tools for working with state-of-the-art, pre-trained machine learning models, primarily based on the transformer architecture.
4. JAX A Python library for accelerator-oriented array computation and program transformation, designed for high-performance numerical computing and large-scale machine learning.
5. Scikit-learn Essential for projects involving classical machine learning methods, providing reliable tools for regression, classification, clustering, dimensionality reduction, and model evaluation.
6. ONNX A universal model-exchange format that allows models trained in one framework to be deployed in another. ONNX Runtime provides highly optimized inference across GPUs, CPUs, edge devices, and browsers via WebGPU.
7. Ray At the center of the world's most powerful AI platforms, precisely orchestrating infrastructure for any distributed workload on any accelerator at any scale.
8. FastAPI A high-performance web framework, easy to learn, fast to code, and ready for production.
9. LangChain Provides a powerful way to build applications that use large language models as reasoning engines.
10. Keras A deep learning API designed for human beings, not machines — focused on debugging speed, code elegance and conciseness, maintainability, and deployability.