Serving Tensorflow Models with a REST API
Offered By: Coursera Project Network via Coursera
Course Description
Overview
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.
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.
Taught by
Charles Ivan Niswander II
Related Courses
Creative Applications of Deep Learning with TensorFlowKadenze Creative Applications of Deep Learning with TensorFlow III
Kadenze Creative Applications of Deep Learning with TensorFlow II
Kadenze 6.S191: Introduction to Deep Learning
Massachusetts Institute of Technology via Independent Learn TensorFlow and deep learning, without a Ph.D.
Google via Independent