SPEAKERS

Peter Skomoroch

Former Head of AI Automation at Workday

Peter Skomoroch is an entrepreneur, investor, and the former Head of Data Products at Workday and LinkedIn. He was Co-Founder and CEO of SkipFlag, a venture backed deep learning startup acquired by Workday in 2018. Peter is a senior executive with extensive experience building and running teams that develop products powered by data and machine learning. He was an early member of the data team at LinkedIn, the world’s largest professional network with over 500 million members worldwide. As a Principal Data Scientist at LinkedIn, he led data science teams focused on reputation, search, inferred identity, and building data products. He was also the creator of LinkedIn Skills and Endorsements, one of the fastest growing new product features in LinkedIn’s history.

Before joining LinkedIn, Peter was Director of Analytics at Juice Analytics and a Senior Research Engineer at AOL Search. In a previous life, he developed price optimization models for Fortune 500 retailers, studied machine learning at MIT, and worked on Biodefense projects for DARPA and The Department of Defense. Peter has a B.S. in Mathematics and Physics from Brandeis University and research experience in Machine Learning and Neuroscience.

Wednesday, October 2
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10:40 am
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11:05 am
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Robertson 2
How do you set up machine learning projects to be successful, and what are the systems and processes you need to put in place to get the most out of your AI platform? This talk will describe the key patterns many organizations have followed to start shipping ML at scale. The most successful machine learning applications are directly tied to improving metrics that matter for your business, but how do you identify and prioritize those projects? How do you power your new AI platform when you can't get access to training data or organizational resources? We'll describe how to make a clear business case for AI projects, build a prioritized roadmap, and make foundational investments in areas like data pipelines, tracking, analytics, monitoring, and data quality. With the right conditions in place, you will see outsized impact across your company as new data products and ML models are rapidly deployed at scale.
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