Serving Tensorflow Models with a REST API

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In this Guided Project, you will:

Create and save Tensorflow models as servable objects

Integrate custom functions into servables

Serve TF servables using conforming to REST

Clock2 hours
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

In this project-based course, you will learn step-by-step procedures for serving Tensorflow models with a RESTful API. We will learn to save a Tensorflow object as a servable, deploy servables in Docker containers, as well as how to test our API endpoints and optimize our API response time. I would encourage learners to experiment with the tools and methods discussed in this course. The learner is highly encouraged to experiment beyond the scope of the course. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Skills you will develop

Web ApplicationPython ProgrammingTensorflowRepresentational State Transfer (REST)model optimization

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Define basic terminology

  2. Saving our model in the SavedModel format

  3. Serving the Model: Server Side

  4. Serving the Model: Client Requests

  5. Using Docker for serving

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

Frequently asked questions

Frequently Asked Questions

More questions? Visit the Learner Help Center.