Deploy Models with TensorFlow Serving and Flask
7,731명이 이미 등록했습니다.
7,731명이 이미 등록했습니다.
In this 2-hour long project-based course, you will learn how to deploy TensorFlow models using TensorFlow Serving and Docker, and you will create a simple web application with Flask which will serve as an interface to get predictions from the served TensorFlow model. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your Internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with (e.g. Python, Jupyter, and Tensorflow) pre-installed. Prerequisites: In order to be successful in this project, you should be familiar with Python, TensorFlow, Flask, and HTML. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - 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.
작업 영역이 있는 분할 화면으로 재생되는 동영상에서 강사는 다음을 단계별로 안내합니다.
작업 영역은 브라우저에 바로 로드되는 클라우드 데스크톱으로, 다운로드할 필요가 없습니다.
분할 화면 동영상에서 강사가 프로젝트를 단계별로 안내해 줍니다.
GS 제공2020년 4월 10일
More oriented toward using flask than on TensorFlow Serving but well done.
RB 제공2020년 6월 16일
Nice way to get started with model deployment with web app.
MS 제공2020년 9월 14일
This course helped me a lot, I was confused and looked up a lot of articles on deploying deep learning models with tensorflow but this one helped by a great margin.
JL 제공2020년 6월 26일
Time given for the virtual desktop is not enought if you actually type and try everything he does.