Deploy Models with TensorFlow Serving and Flask
Serve a TensorFlow model with TensorFlow Serving and Docker.
Create a web application with Flask to work as an interface to a served model.
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.
Skills you will develop
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:
Introduction
Getting Started with the Flask App
Index Template
TensorFlow Serving
Getting Predictions
Connecting to Model Server
Displaying the Results
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
Reviews
TOP REVIEWS FROM DEPLOY MODELS WITH TENSORFLOW SERVING AND FLASK
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.
The virtual machine could be properly configured so as not to waste time on problems that arise. Also, I found the Rhyme platform with bugs
Time given for the virtual desktop is not enought if you actually type and try everything he does.
This workshop is very helpful but I would have liked something a bit more advanced.
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