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Machine Learning on AWS with TensorFlow and SageMaker

Published by O'Reilly Media, Inc.

Intermediate to advanced content levelIntermediate to advanced

Accelerate your ML tasks by moving them to the cloud

This live event utilizes Jupyter Notebook technology

It seems everywhere you turn nowadays you will hear something about Machine Learning and AI and there is a good reason for it. Businesses are finding new ways to make improvements and solve problems through leveraging Machine Learning. Amazon Web Services has created tools like Amazon SageMaker to lower the ceiling with the skills required to get working with complex Machine Learning models and algorithms.

Skills involved with Machine Learning and Amazon SageMaker are in high demand throughout the industry. Taking this course and continuing your education on these topics will help set you on a path to becoming a Machine Learning expert.

In this live session, we will provide a brief introduction to Amazon SageMaker. This will help provide you an entry point to get started with built-in models and begin using your own models. It will also provide you with enough information on the next steps in continuing your education and research on Amazon SageMaker and Machine Learning.

What you’ll learn and how you can apply it

By the end of this live, hands-on, online course, you’ll understand:

  • Learn how to perform Data Engineering tasks on AWS
  • Learn how to perform Machine Learning Modeling tasks on the AWS platform
  • Learn how to work with AWS SageMaker

And you’ll be able to:

  • Leverage your existing Machine Learning skills on AWS SageMaker
  • Use Amazon SageMaker built-in algorithms to build ML models
  • Use your own models with Amazon SageMaker

This live event is for you because...

  • You are a Data Scientist who needs to run ML models in the cloud.
  • You are a Product Manager who understands AWS Machine Learning Life Cycles.
  • You are a Machine Learning Engineer who wants to work with AWS Machine Learning Tools.
  • You are a Software Engineer looking to leverage AWS Machine Learning Tools.

Prerequisites

  • Ideally 1-2 years of experience with AWS and six months of experience using machine learning tools.

Recommended preparation:

  • No preparation is needed to simply attend and follow along. However, in order to complete the exercises, you will need an AWS account. Most likely, the use of AWS Sagemaker will incur costs on your AWS account.

Recommended follow-up:

Schedule

The time frames are only estimates and may vary according to how the class is progressing.

Machine Learning and Deep Learning and TensorFlow Review (50 minutes)

  • Presentation: Overview of Machine Learning, Deep Learning, & TensorFlow
  • Presentation: How are you or plan on applying TensorFlow?
  • Poll: Quiz on lesson material
  • Q&A
  • Break (10 Minutes)

Introducing AWS SageMaker (50 minutes)

  • Presentation: Intro to AWS SageMaker
  • Exercise: Demo SageMaker and Jupyter Notebooks
  • Q&A
  • Break (10 Minutes)

Using Built-In Algorithms in SageMaker (50 minutes)

  • Presentation: Discuss ML algorithms
  • Exercise: Demo on ML algorithms
  • Q&A
  • Break (10 Minutes)

Use your own Algorithms or Models with Amazon SageMaker (50 minutes)

  • Presentation: Discuss going beyond the built-in Algorithms with using your own
  • Exercise: Demo a use case on using a custom model
  • Q&A

Q&A (10 Minutes)

Your Instructor

  • Richard Augenti

    Richard Augenti is an entrepreneur and a professional AWS cloud DevOps engineer. He founded Phoenix Rising Solutions to train students on new and emerging technologies pertaining to cloud and other advanced technologies. Previously, he worked for multiple Fortune 500 companies, including two of the top ten cloud-managed service providers. Richard is also an experienced elearning instructor and training consultant, who’s had the opportunity to work for companies like Cloud Academy and Linux Academy, along with doing onsite consulting engagements for large corporate clientele. Richard has 25 years of IT and cloud experience, which he integrates into his training materials with the goal of assisting students with becoming prepared for real world technical roles.

Skills covered

  • Machine Learning
  • TensorFlow