TensorFlow Serving Training Course

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Course Code



7 hours (usually 1 day including breaks)


  • Experience with TensorFlow
  • Experience with the Linux command line


  • Developers
  • Data scientists


TensorFlow Serving is a system for serving machine learning (ML) models to production.

In this instructor-led, live training (online or onsite), participants will learn how to configure and use TensorFlow Serving to deploy and manage ML models in a production environment.

By the end of this training, participants will be able to:

  • Train, export and serve various TensorFlow models
  • Test and deploy algorithms using a single architecture and set of APIs
  • Extend TensorFlow Serving to serve other types of models beyond TensorFlow models

Format of the course

  • Part lecture, part discussion, exercises and heavy hands-on practice

Course Customization Options

  • To request a customized training for this course, please contact us to arrange.

Course Outline

TensorFlow Serving Overview

  • What is TensorFlow Serving?
  • TensorFlow Serving architecture
  • Serving API and REST client API

Preparing the Development Environment

  • Installing and configuring Docker
  • Installing ModelServer with Docker

TensorFlow Server Quick Start

  • Training and exporting a TensorFlow model
  • Monitoring storage systems
  • Loading exported model
  • Building a TensorFlow ModelServer

Advanced Configuration

  • Writing a config file
  • Reloading Model Server configuration
  • Configuring models
  • Working with monitoring configuration

Testing the Application

  • Testing and running the server

Debugging the Application

  • Handling errors

TensorFlow Serving with Kubernetes

  • Running in Docker containers
  • Deploying serving clusters

Securing the Application

  • Hiding data


Summary and Conclusion



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