How do I use TensorFlow Serving to deploy a trained deep learning model into production for real-time inference

Question

With the help of code can you tell me How do I use TensorFlow Serving to deploy a trained deep learning model into production for real-time inference?

Answer 1

To deploy a trained deep learning model with TensorFlow Serving, export the model in SavedModel format, run TensorFlow Serving as a REST or gRPC server, and send inference requests via an API client.

Here is the steps you can follow:

• 1. Train & Save a Model in SavedModel Format

• 2. Start TensorFlow Serving Locally

• 3. Send a Prediction Request Using Python

In the above code we are using the following key approaches:

• Model Training & Exporting:

  • A Keras model is trained and saved in SavedModel format for TensorFlow Serving.

• Running TensorFlow Serving:

  • The model is served using Docker with TensorFlow Serving, exposing a REST API.

• Making Real-Time Predictions:

  • Uses a POST request to send inference data and get predictions from the deployed model.

• Scalability & Production Readiness:

  • Supports batch requests, gRPC, and RESTful API for scalable deployment.

Hence, TensorFlow Serving enables fast, scalable, and real-time deployment of deep learning models via REST and gRPC, making it ideal for production environments.