Machine Learning: From Front End to Inference

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This lab demonstrates how to build out a machine learning application that can accept data from any type of Internet connected device that can make a POST request. Once completed, this lab will demonstrate an application that can make real time predictions based on the data submitted.

Level

Intermediate

Duration

1 Hours 30 Minutes

Course Objectives

In this course, you will learn how to:

• Understand how S3 is used in a machine learning environment
• Describe the role of Juypter Notebooks and how they can be used to build/train/deploy machine learning environment assets
• Identify IAM permissions needed for each service
• Describe how an API Gateway is used to control access to backend resources
• Describe how a Lambda function is used to respond to requests from an API Gateway request and return a prediction from the machine learning environment
  
  

Intended Audience

This course is intended for:

• Architects
• Data Engineers

Prerequisites

We recommend that attendees of this course have the following prerequisites:

• Familiar with basic navigation of the AWS Management Console
• Comfortable editing scripts using a text editor

Course Outline

• Task 1: Model Data storage
• Task 2: IAM, Roles and Permissions
• Task 3: Jupyter Notebooks
• Task 4: Lambda
• Task 5: API Gateway