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Peter Bray

CEO at Versionista
Talk title:
Practical machine learning to accelerate data intelligence
29th Sep
9:30 am
Who should attend: Data intelligence geeks
What you will learn: How to capitalize machine learning to get faster and better insights
Insights from data can lead to surprising, even counterintuitive assumptions.

With ML and NLP, algorithms can be designed to distinguish meaningful conclusions from meaningless ones. Collecting insights from data using machine learning has allowed businesses to channel their efforts and stay ahead of the future.

Particularly in web data extraction, ML has allowed us to challenge our conventions and enter a new domain of data extraction that is more efficient.

Are you looking to capitalize ML to generate insights that set you apart? 

Join Peter Bray to get practical insights on how machine learning can help you get better, faster, and more accurate insights from crawl data.

In this talk, Peter will show you how to:

  • Leverage NLP (Natural Langauge Processing) and ML (Machine Learning) techniques to accelerate strategies to crawl, extract, classify, and prioritize insights from publicly available information.
  • Get hands-on technical and practical solutions for using ML to improve insights covering topic modeling, object recognition, and more.
  • Explore a new world of data extraction.

Speaker Bio

Peter Bray is the CEO of Versionista, a SaaS business that provides web change monitoring and ML data intelligence. As a hands-on engineer, he is instrumental in key architecture in Versionista's products, including Fluxguard, a solution for multi-step, multi-page orchestration for complex web change intelligence.
Peter and his team provide data intelligence and machine learning engineering for the FTC, State Department, US Air Force, US Department of Defense, and notable commercial brands. Previously, Peter founded the largest West Coast digital agency in the Web 1.0 era.
He has also successfully launched many cross-functional and multi-region enterprise initiatives with forecasts and data explorations via explainable machine learning and natural language processing.
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