AI frameworks provide the essential tools and libraries needed to build, train, and deploy machine learning and deep learning models. This blog offers a comprehensive comparison of leading AI frameworks, including TensorFlow, PyTorch, Keras, MXNet, and others. Understand each framework's strengths, learning curve, and ideal use cases—from rapid prototyping to large-scale production systems. Learn how AI developers choose the right framework based on project needs, team expertise, and hardware compatibility. Discover how frameworks enable streamlined workflows for data preprocessing, model evaluation, and deployment. Whether you're building neural networks, NLP applications, or computer vision models, this blog highlights how the right AI framework accelerates development and ensures scalable performance.