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Machine Learning on AWS with TensorFlow and SageMaker

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Accelerate your ML tasks by moving them to the cloud

Richard Augenti

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 training course 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.


  • 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:

About your instructor

  • Richard Augenti is a Senior Cloud DevOps Engineer and E-Learning Trainer with a wide range of experience working with cloud technologies such as Amazon Web Services, Microsoft Azure and Google Cloud Platform. He has worked for multiple Fortune 500 companies focused on client engagements that involved architecting cloud infrastructure and DevOps pipelines for various types of client environments. He has a strong focus on leveraging Infrastructure as Code and configuration tools to automate and manage cloud environments.

    Richard is the Founder and CEO of Phoenix Rising Solutions which provides e-learning and live online and onsite training classes. His professional training experience include working for companies like Cloud Academy and Linux Academy where he created e-learning courses on Azure, AWS, Google Cloud, and DevOps tools.


The timeframes 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)