Introduction to Machine Learning with AWS

In this workshop, I will introduce machine learning using cloud services. We'll explore machine learning concepts and see first-hand the steps to process and prepare data for machine learning, train and deploy a machine learning model, and generate real-time predictions. We will explore Amazon SageMaker, a tool used to build, train, deploy, and manage models at scale. You'll walk away understanding how to leverage machine learning to solve business problems and the services available to build machine learning solutions. No Ph.D. or prior cloud or machine learning experience is required!

Technologies covered:

Amazon Web Services (AWS), Amazon SageMaker, Amazon S3, AWS Lambda, and Amazon CloudWatch

Intended Audience:

The workshop is intended for developers and software engineers looking to transition to machine learning.

Day 1: Machine learning and data

• Machine learning overview and benefits
• Machine learning in the cloud
• Real-world uses of machine learning
• Workshop project overview
• Machine learning lifecycle
• Collect and store training data
• Prepare and clean training data
• Visualize and analyze training data

Day 2: Model training and deployment

• Feature Engineering
• Built-in learning algorithms
• Train and tune the model
• Evaluate and qualify the model
• Deploy and host the model
• Make predictions using the model
• Monitor and debug the model
• Detect and prevent bias

Pre-requisites

The ability to read basic Python code is necessary, or familiarity with another programming language is strongly recommended. No prior experience with machine learning or AWS is required.

Computer setup:

This workshop requires attendees to bring a laptop. Attendees are strongly encouraged to create an AWS Free Tier account before the workshop to take advantage of the two-month free trial of Amazon SageMaker.