AI frameworks are essential tools that simplify the development, training, and deployment of machine learning and deep learning models. This blog covers popular AI frameworks such as TensorFlow, PyTorch, Keras, and Scikit-learn, explaining their features, advantages, and best use cases. Learn how each framework supports different stages of AI development—from research prototyping to production-ready applications. Understand how framework selection impacts performance, scalability, and integration with other tools. We also compare ease of use, community support, and hardware compatibility. Whether you’re building neural networks, training language models, or deploying AI at scale, choosing the right framework is a key development decision.