TensorFlow: Machine Learning Framework for Production AI Models

TensorFlow provides end-to-end machine learning: data preprocessing (tf.data), model building (Keras API), training (distributed training, GPU/TPU support), evaluation, and deployment (TF Serving for APIs, TF Lite for mobile, TF.js for browser). TensorFlow excels at: production ML serving (TF Serving is battle-tested), computer vision (image classification, object detection), and edge deployment (TF Lite model optimization).

Enterprise TensorFlow requires: proper model architecture selection (CNNs for images, transformers for NLP, GBMs for tabular), training pipeline automation, model versioning and registry, serving infrastructure with auto-scaling, and monitoring for model drift and accuracy degradation. TensorFlow vs PyTorch selection depends on: deployment target (TF for production serving, PyTorch for research flexibility), team expertise, and ecosystem requirements.