In this liveProject, you’ll deploy a completed deep learning model so that it can be easily used by your clients. You’ll use the Flask Python library to build a web page that will host your model, and use HTML, CSS, and JavaScript to build a simple web interface. Finally, you’ll take your model out into the world by using ngrok to make locally-served web pages available anywhere.
This project is designed for learning purposes and is not a complete, production-ready application or solution.
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project author
Mark Ryan
Mark Ryan has a Bachelor in Computer Science from the University of Waterloo and an MSc in Computer Science (with a specialty in Artificial Intelligence) from the University of Toronto. As a senior leader for IBM's Db2 relational database, he was immersed in structured, tabular data. Mark is currently a Data Science Manager at Intact Insurance. In addition to deep learning, Mark's interests include chatbots and transformer-based language models.
prerequisites
This liveProject is for intermediate data scientists looking to expand their skill set with applied deep learning capabilities. To begin this liveProject, you will need to be familiar with:
TOOLS
Intermediate Python
Beginner pandas
Novice in Flask
Novice in ngrok
Basics of HTML, CSS, and JavaScript
TECHNIQUES
Invoking deep learning models
Basic web development
features
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