White Papers

How to Design Your Model Development Life Cycle

In This White Paper

According to Gartner Research, by 2020, more than 40% of data science tasks will be automated, resulting in increased productivity and broader usage by citizen data scientists.

In this educational white paper “How to Design Your Model Development Lifecycle”, the second installment of our “Lab to Factory” series, is focused on building Machine Learning capabilities across the enterprise. Rehgan and Garrett will help us understand:

  • Trends driving the adoption of machine learning in today’s market
  • How to define the Model Development Life Cycle
  • Common challenges migrating a model downstream to user acceptance testing
  • Best Practices for a User Acceptance Testing transition checklist

Garrett Long

Garrett Long is the Vice President of Business Development at Open Data Group. He holds a BS in Electrical Engineering from Carnegie Mellon University. At Open Data Group, Garrett focuses on helping customers understand, develop and implement use cases leveraging FastScore.

Rehgan Avon

Rehgan Avon is Product Manager at Open Data Group, with a background in integrated systems engineering. Her focus is on analytical technology, migrating from data and systems engineering to product strategy. Rehgan previously held positions in simulation engineering and operations at United Airlines and Honda. She has taught introduction courses in Data Science and Web Development at different MBA programs across the US. She is also the founder and coordinator for the first Women in Analytics conference in Ohio. She remains active and involved in fostering collaboration around emerging analytical methods and technologies.