SPEAKERS

Pallav Agrawal

Director Data Science
Levi Strauss & Co.

As the Director of Data Science at Levi’s, Pallav builds Human-Centered Machine Learning applications, and spends most of his time basking in the reflected glory of his team’s accomplishments. He considers himself an outsider to the field of Data Science and arrived in the Bay area in 2010 to actually become a Structural Engineer. Starting off as a Catastrophe Modeler, predicting Hurricane tracks and assessing Earthquake aftereffects, Pallav found himself enamored with the process of extracting meaningful signals from the noisy world we live in that eventually led him to his current occupation.

Pallav is a part-time Design Thinking coach and has helped non-profits and early-age startups develop clarity on their mission and recognize growth areas. He is an avid follower of Seth Godin, Ken Robinson, and Nicholas Taleb, and is currently looking at ways to explain algorithms through cute, anthropomorphized animals.

Tuesday, October 1
|
2:00 pm
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2:25 pm
|
Robertson 1
In this talk, we will cover the journey we undertook to go from a fully outsourced model to over a dozen internally-built Machine Learning models deployed in production that are ROI positive and that solve real business problems, in about two years. We will discuss challenges faced along the way, key design methodologies and technologies utilized, experimentation approaches, and how we established a culture of constant learning, iteration and improvement. We will also look at how we brought business stakeholders along the journey while ensuring the Data Science team delivered quick wins to gain credibility and simultaneously built the foundation to improve productivity.
Tuesday, October 1
|
3:55 pm
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4:25 pm
|
Robertson Auditorium
Traditional enterprises are often burdened by complex legacy systems, regulatory requirements, talent shortages, and other factors that make scaling machine learning more difficult than at startups. In this panel we discuss how traditional companies in a variety of industries can overcome these challenges and more successfully deliver data science and machine learning models into production.
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