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Building Machine Learning Models with AWS Sagemaker

Developing a longevity prediction model in AWS Sagemaker and selling it into the Marketplace

Noah Gift
Michelle Davenport

Learn to build and sell machine learning models on the newly created AWS Machine Learning Marketplace using AWS Sagemaker. The Apple App Store is a popular and lucrative market for mobile app developers. Similarly, there is an emerging marketplace for pre-trained machine learning models and algorithms on AWS Marketplace. This training takes you through the process of building machine learning models for sale in the AWS Marketplace. Everyone from developers, data engineers, product managers and data scientists will find valuable insights into getting started in this emerging marketplace.

The example project will cover the health care space. Currently, the healthcare industry is flooded with data that is being unused by hospital systems and healthcare providers, usually to the detriment of patients and the facility’s finances. There are ways to optimize this data for the purposes of health prediction, in order to provide business intelligence for hospital system administrators that can quantify risk and prioritize care. Using a longitudinal longevity dataset as an example, we’ll discuss the obstacles in the way of nutrition research and discovery and the ways in which machine learning can be applied to high-powered health prediction. We’ll go through the necessary steps of discussing the potential pitfalls in utilizing health datasets, performing exploratory data analyses, executing predictive models, and evaluating the models to reach a conclusion.

What you'll learn-and how you can apply it

  • Perform Exploratory Data Analysis on Sagemaker
  • Build a longevity prediction model in Sagemaker
  • Learn to package Sagemaker models for sale in AWS Marketplace
  • Understand high-level managed machine learning systems

This training course is for you because...

  • You work as a medical technician or medical professional and want to learn about AWS Machine Learning technologies
  • You work as a software engineer, are new to Machine Learning and want to see how a real-world system could be built without technical debt
  • You work as a Business Analyst, are interested in applied Data Science and want to understand real-world model building
  • You work in Product Management, Sales or Business Development and want to learn how to build and sell machine learning models on the AWS platform

About your instructor

  • Noah Gift is a lecturer in the University of California, Berkeley, graduate data science program, the Northwestern University graduate data science program, and the MSBA program at the University of California, Davis, Graduate School of Management. He consults with startups and other companies on machine learning and cloud architecture and does CTO-level consulting as the founder of Pragmatic AI Labs. Noah has approximately 20 years’ experience programming in Python and is a Python Software Foundation Fellow. Previously, he worked for a variety of companies in roles such as CTO, general manager, consulting CTO, and cloud architect. He’s published over 100 technical publications, including books on cloud machine learning and DevOps, for O’Reilly, Pearson, DataCamp, Udacity, and other publishers. He’s also a certified AWS Solutions Architect. Noah earned an MBA from the University of California, Davis, an MS in computer information systems from California State University, Los Angeles, and a BS in nutritional science from Cal Poly, in San Luis Obispo. You can find more about Noah by following him on GitHub, visiting his website, or connecting with him on LinkedIn.

  • Michelle Davenport earned her PhD in Nutrition from New York University, and received her clinical training as a registered dietitian from the University of California, San Francisco. Upon returning to her roots in Silicon Valley, Michelle earned her reputation as one of the most sought after nutrition experts in food and tech. Previously the President and Co-founder of Raised Real, she created a venture-funded, tech-driven, subscription food program for children that targets infant nutritional milestones. As the fastest growing kids’ food brand in the US, Raised Real currently delivers to thousands of families nationwide. Prior to Raised Real, she worked on the founding team as the Director of Nutrition for Zesty (acquired by Square), where she developed the food and nutrition API and a proprietary nutrient analysis program.


The timeframes are only estimates and may vary according to how the class is progressing

Part 1: Exploratory Data Analysis & Model Building in Sagemaker

Length: 90 min

  • Framing the problem
  • Performing EDA on Nutritional Science dataset around longevity
  • Performing Modeling: Training Sagemaker Models
  • Evaluating Model
  • Conclusion

Q&A: 15 min

Break: 15 min

Part 2: Deploying and Packaging Sagemaker models

Length: 45 min

  • Learn options and best practices for deploying Sagemaker models
  • Develop and Package Sagemaker Models
  • Using Elastic Container Registry with Sagemaker models and Docker containers

Q&A: 15 min

Break: 5 min

Part 3: List and Monetize Sagemaker Product

Length: 45 min

  • Adding descriptions, promotions and pricing information
  • Learn about global pricing strategy
  • Learn about the details of the ML listing process

Q&A: 15 min