AI Frameworks & Development Kits

Framework for developing applications powered by language models, enabling composition and integration.

Open-source machine learning framework developed by Google for building and training ML models.

Open-source machine learning library based on Torch, used for applications such as computer vision and NLP.

High-performance numerical computing library with automatic differentiation for ML research, especially on accelerators.

High-level neural networks API that can run on top of TensorFlow, JAX, or PyTorch. Known for user-friendliness.

Library of pre-trained models and tools for NLP tasks, facilitating easy download, use, and fine-tuning of models.

A toolkit for developing and comparing reinforcement learning algorithms. Provides a standard API for environments.

Unified MLOps platform to build, deploy, and manage ML models at scale on Google Cloud, with SDKs and tools.

An open-source framework from Firebase for building, deploying, and monitoring AI-powered features and applications.

Open-source framework for building cross-platform, customizable ML solutions for live and streaming media.

A modern, fast (high-performance), web framework for building APIs with Python 3.7+ based on standard Python type hints.

An open-source Python library that makes it easy to create and share beautiful, custom web apps for machine learning and data science.

Quickly build UIs for your machine learning models, functions, or any Python code with a few lines of code.

Framework for building, shipping, and scaling AI applications. Standardizes model packaging and deployment.

A programming model for foundation models that separates the flow of a program from the parameters of each step.