Serving Tensorflow Models with a REST API

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在此指导项目中,您将:

Create and save Tensorflow models as servable objects

Integrate custom functions into servables

Serve TF servables using conforming to REST

Clock2 hours
Intermediate中级
Cloud无需下载
Video分屏视频
Comment Dots英语(English)
Laptop仅限桌面

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.

您要培养的技能

TensorflowPython ProgrammingRepresentational State Transfer (REST)

分步进行学习

在与您的工作区一起在分屏中播放的视频中,您的授课教师将指导您完成每个步骤:

  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

指导项目工作原理

您的工作空间就是浏览器中的云桌面,无需下载

在分屏视频中,您的授课教师会为您提供分步指导

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