👉 TensorFlow Engineering, often abbreviated as TF Engineering, is a specialized subset of TensorFlow designed to streamline the development, deployment, and management of machine learning applications in production environments. It focuses on providing tools and frameworks to handle the complexities of real-world deployment, such as scalability, reliability, and monitoring. TF Engineering includes features like TensorFlow Serving for efficient model serving, TensorFlow Extended (TFX) for end-to-end machine learning pipelines, and TensorFlow Lite for deploying models on edge devices. It also emphasizes observability with tools like TensorBoard and integrates seamlessly with cloud platforms and container orchestration systems like Kubernetes, enabling engineers to deploy and manage ML models at scale with ease.
