ML frameworks

ML frameworks are the structured, pre-built toolsets (TensorFlow, PyTorch, Scikit-learn) that simplify the entire machine learning lifecycle: data preparation, model training, and deployment.

ML frameworks deliver the comprehensive toolset for the entire machine learning pipeline. These platforms abstract away complex mathematical and statistical algorithms, providing pre-built functions for data preparation, model building, and optimization. They are engineered for performance: supporting distributed training and GPU/TPU acceleration for deep learning models, particularly with major players like TensorFlow and PyTorch. This streamlined approach allows engineers to focus on model architecture and deployment rather than coding every algorithm from scratch.

https://en.wikipedia.org/wiki/Machine_learning