AI frameworks are essential tools that help developers build, train, and deploy machine learning and deep learning models efficiently. This blog reviews popular AI frameworks like TensorFlow, PyTorch, Keras, and Scikit-learn, highlighting their strengths, use cases, and ecosystems. Learn how these frameworks simplify complex processes such as data preprocessing, model training, and performance tuning. Understand the differences between high-level APIs for rapid development and low-level libraries for custom solutions. With the growing demand for AI across industries, selecting the right framework is crucial for scalability, experimentation, and deployment. Explore how businesses can accelerate innovation through robust, well-supported AI development environments.