Python Frameworks for AI Systems
- 01. PyTorch is a deep learning framework preferred by researchers and applied scientists.
- 02. TensorFlow makes it easy to create ML models that can run in any environment
- 03. The Hugging Face Transformers library is an open-source Python library that provides APIs and tools for working with state-of-the-art, pre-trained machine learning models, primarily based on the transformer architecture
- 04. JAX is a Python library for accelerator-oriented array computation and program transformation, designed for high-performance numerical computing and large-scale machine learning.
- 05. Scikit-learn is essential for projects involving classical machine learning methods. It provides reliable tools for regression, classification, clustering, dimensionality reduction and model evaluation.
- 06. ONNX serves as a universal model-exchange format that allows models trained in one framework to be deployed in another. ONNX Runtime provides highly optimised inference support across a wide range of hardware, including GPUs, CPUs, edge devices and even browsers through WebGPU.
- 07. Ray is at the center of the world's most powerful AI platforms. It precisely orchestrates infrastructure for any distributed workload on any accelerator at any scale.
- 08. FastAPI framework, high performance, easy to learn, fast to code, ready for production
- 09. LangChain provides a powerful way to build applications that use large language models as reasoning engines
- 10. Keras is a deep learning API designed for human beings, not machines. Keras focuses on debugging speed, code elegance & conciseness, maintainability, and deployability.