Machine learning frameworks are essential tools that simplify the development and deployment of machine learning models. These frameworks provide pre-built components, libraries, and tools that enable data scientists and machine learning engineers to efficiently build, train, and evaluate models. With the increasing complexity of machine learning tasks, frameworks like TensorFlow, PyTorch, and Scikit-Learn have emerged to streamline processes, enhance productivity, and foster collaboration. By leveraging these frameworks, practitioners can focus on solving specific problems rather than getting bogged down in the underlying technical details, ultimately accelerating innovation in the field of artificial intelligence and data science.