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Deploy Models with TensorFlow Serving and Flask

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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.

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"Deploy Models with TensorFlow Serving and Flask" is an engaging, intermediate-level project-based course designed for individuals passionate about IT and computer science. Over the span of 2 hours, you'll dive into deploying TensorFlow models using TensorFlow Serving facilitated by Docker. Additionally, you'll develop a straightforward web application with Flask, which will seamlessly interface to obtain predictions from the deployed TensorFlow model. This course, offered by Coursera, provides a practical, hands-on learning experience, and is available to learners at no cost. It is ideal for those who have a foundational understanding of machine learning and web development, and are eager to enhance their skills in model deployment and web interfacing.