Taking food preferences as example, Using ML and AI, and consuming the data feed exposed on various social media, is possible to automatically generate a model to describe the food preferences of any user at any time. With such model, it is possible to provide contextual recommendations that matter to a foodie.
What will Learn?
you will learn how to define, build, train, and deploy a machine learning model on the Amazon SageMaker infrastructure, how to feed the model with the data that come from a user social media accounts, and how to consume the SageMaker endpoints and display the food recommendations in an Android app
Giorgio is a software engineer well-versed in the full software development lifecycle—including team building, requirements definition, requirements prioritizing, design, interface implementation, testing, deploying, and maintenance—and a passion for creating highly interactive and contextual end user experiences on mobile, web, desktop.
Experience includes author, educator, community leader and Engineering Lead at Akamai Technologies and earlier at McGraw-Hill Education