If you're looking for a machine learning API that spans multiple backend frameworks and works on multiple surfaces, Keras is a good choice. It's designed to be fast to code and debug, with a consistent and easy-to-use API across frameworks like JAX, TensorFlow and PyTorch. It's also highly scalable and suitable for large-scale industrial use, so it's a good choice for computer vision, natural language processing and generative AI.
Another good choice is PyTorch. PyTorch is a flexible and powerful end-to-end machine learning framework that's good for rapid experimentation and efficient production. It's got scalable distributed training and can run on iOS and Android devices, so it's good for a lot of different tasks. It also comes with a lot of libraries and tools for model interpretability and deep learning on irregular input data.
For an open-source platform, TensorFlow is also worth a look. TensorFlow has multiple levels of abstraction, including the high-level Keras API, eager execution for immediate iteration, and the Distribution Strategy API for distributed training. It can run on-device machine learning and on a variety of hardware configurations, and it's widely used in tech, health care and education.
Last, MindsDB is a development platform that connects to a variety of data sources and popular AI/ML frameworks, letting you build fast, secure and scalable AI-powered apps. It spans a variety of environments and has features like multiple AI engines, model management and automation, so it's a good choice for companies that want to quickly adopt AI and automate tasks.