Serving Tensorflow Models with a REST API

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En este Proyecto guiado, tú:

Create and save Tensorflow models as servable objects

Integrate custom functions into servables

Serve TF servables using conforming to REST

Clock2 hours
IntermediateIntermedio
CloudNo se necesita descarga
VideoVideo de pantalla dividida
Comment DotsInglés (English)
LaptopSolo escritorio

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.

Habilidades que desarrollarás

Web ApplicationPython ProgrammingTensorflowRepresentational State Transfer (REST)model optimization

Aprende paso a paso

En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:

  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

Cómo funcionan los proyectos guiados

Tu espacio de trabajo es un escritorio virtual directamente en tu navegador, no requiere descarga.

En un video de pantalla dividida, tu instructor te guía paso a paso

Preguntas Frecuentes

Preguntas Frecuentes

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