Distributed Machine Learning services

Nov 14, 10:00AM PDT(06:00PM GMT).
  • Free 151 Attendees
Machine Learning and Artificial Intelligence represent an exciting opportunity for all of us to stop working on repetitive tasks, to improve the velocity and precision of data analysis, and to get an insight on complex and simple problems of everyday life.

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 Natili

Software developerment manager at Amazon.

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

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