TensorFlow Serving

• Experience with TensorFlow
• Experience with the Linux command line

Audience

• 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.

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

Troubleshooting

Summary and Conclusion