Deploy Models with TensorFlow Serving and Flask
7731 ya inscrito
7731 ya inscrito
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.
En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:
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
por RB16 de jun. de 2020
Nice way to get started with model deployment with web app.
por MS14 de sep. de 2020
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.
por JL26 de jun. de 2020
Time given for the virtual desktop is not enought if you actually type and try everything he does.
por GS10 de abr. de 2020
More oriented toward using flask than on TensorFlow Serving but well done.