What is TensorFlow’s role in deploying machine learning models to production environments?

The major role of TensorFlow in the production environment is deployment, providing tools and frameworks that support the whole deployment process. With TensorFlow Serving, it can enable the fast and flexible deployment of models for real-time predictions in production environments. TensorFlow Lite allows the model to be optimized and deployed on mobile and embedded devices, while the TensorFlow.js runtime enables deployment directly in web browsers. Further, TensorFlow Extended provides an end-to-end pipeline for data validation, model training, evaluation, and serving consistently and in a scalable production workflow. These utilities available within the community make TensorFlow versatile and effective, deploying models on diverse platforms from cloud servers to edge devices.