Midwest Sessions

Data may be the new oil, but it is worthless unless you refine it into insights using machine learning, and use it to power decisions throughout your business. In this opening keynote we unveil how leading companies are outcompeting their peers by transforming themselves from being data-driven to model-driven, by: 

• Unlocking the potential of your experts by embracing a diversity of tools and democratizing access to data and infrastructure
• Accelerating the machine learning lifecycle through collaboration and reuse
• Deploying platforms that are purpose built for scale and governance

Every company should have a business transformation strategy if they want to navigate and succeed in the rapidly changing competitive business landscape.  As new challenges crop up from a consumer driven culture within an accelerated technology field, companies are looking for innovative solutions to gain deeper insight into their customers and to invigorate their line of business.  For data scientists, asking the question of what problems they are trying to solve is just the beginning.   Building the future with AI, while trying to keep-the-lights-on, is a problem that most companies still face today. Find out how our teams of data scientists and engineers embrace change and failure to create a data science platform that delivers some of the most innovative AI solutions in the marketplace.

A data scientist’s perspective: how Gogo leverages data from airborne devices to keep inflight wifi reliable by streamlining maintenance decisions and much more. We’ll share our experience with the ML model life cycle from the initial business problem to cost savings impact and everything in between. This talk offers a look into data-wrangling, model development, and buy-in/adoption with external teams. At each step, we review the approaches taken, sharing what worked, what didn’t and why. We’ll also discuss how successful models can have far-reaching impacts across different business units and provide a springboard for future model development.

This technical session details how supervised and unsupervised learning come together in order to recommend beer. Most American bars have at least one light beer such as Miller Lite or Coors Light on tap, but the rest of the taps, as well as refrigerator space, need to be optimized with the correct products based on customer preferences. In order to do this, data science at MillerCoors collaborates with our sales team to recommend which of our beers should be focused on during the selling process. This talk will discuss how forecasting, collaborative filtering and clustering are used together for getting out the best products at each specific outlet. You’ll also learn how data science can be used to assist sales people in working more effectively.

Machine Learning has integrated itself into almost every product we use. Unfair biases can and do leak into these models, creating the opportunity for harm. Often these unfair model biases come from the data the model is built on and from an unwise assumption by the builder that the data collected is representative of the population and objectively collected. 

In this talk I’ll show you a few examples of where we’ve gone wrong before and how you can reduce or prevent unfair biases from leaking into your data products.

Sonny Tai is a Captain in the Marine Corps Reserves and the CEO / Co-Founder of Aegis AI, a startup that employs computer vision to automatically identify gun threats in existing security camera feeds. Sonny will talk about how his upbringing in South Africa and his experiences in the Marine Corps impacted his decision to start Aegis. He will discuss how Aegis AI overcame the lack of an adequate publicly available data set in building their initial models. Their scrappiness in collecting training data through esoteric sources and creating their own training data allowed them to build a gun recognition model that performed well enough in production to deploy with initial customers and raise millions of dollars in funding from the University of Chicago and numerous venture capital firms.