Once you have trained and exported your model, typically using the SavedModel format discussed previously, the next significant step is making it available to serve predictions. While you could build a custom application (e.g., using Flask or FastAPI) to load the SavedModel and expose an API endpoint, this approach often lacks the robustness, performance, and lifecycle management features required for demanding production environments. This is precisely the problem TensorFlow Serving aims to solve.
TensorFlow Serving is a dedicated, high-performance serving system specifically designed for machine learning models in production. Think of it not just as a library, but as a standalone server application optimized for inference. It takes your trained models (packaged as SavedModels) and makes them accessible over a network via well-defined APIs, typically REST or gRPC.
Deploying models involves more than just loading a file and running model.predict()
. Production systems often face requirements like:
TensorFlow Serving is engineered to handle these challenges effectively. It provides out-of-the-box solutions for managing the lifecycle of your models, allowing you to deploy new versions, run A/B tests between versions, or serve multiple distinct models concurrently, all while maintaining high performance.
While later sections cover practical usage, understanding the basic architecture is helpful. TensorFlow Serving employs several abstractions:
Basic architecture of TensorFlow Serving, showing how a client request flows through the API to the manager, which uses loaders and sources to serve predictions from managed model versions (Servables).
Using TensorFlow Serving offers several advantages:
In essence, TensorFlow Serving provides the infrastructure glue between your trained TensorFlow models and the applications that need to consume their predictions at scale. The following sections will demonstrate how to prepare your models and deploy them using this powerful system.
Was this section helpful?
© 2025 ApX Machine Learning