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O’Reilly Announces O’Reilly Radar: Data & AI to Help Tech Leaders Drive Innovation and Successful Implementation

September 9, 2021

Free Virtual Event to Cover the Latest Developments, Tools, Best Practices, and Critical Issues for Data and AI

BOSTON—September 9, 2021O’Reilly, the premier source for insight-driven learning on technology and business, today announced O’Reilly Radar: Data & AI, a free virtual event focused on showcasing the latest and most important developments across data and AI. The one-day event will take place from 10:00am to 1:30pm ET on Thursday, October 14.

O’Reilly Radar: Data & AI will explore the issues, tools, and best practices integral to data and AI innovation and implementation. The online event will include two keynote sessions and two concurrent three-hour tracks offering new insights and best practices for organizations at any phase of their data and AI journeys. Topics of discussion will include prototyping and pipelines to deployment, DevOps, and responsible and ethical AI. In addition, Tim O’Reilly, O’Reilly’s founder and CEO, will deliver the closing address, “The Future of Data and AI.”

“We are at the beginning of an explosion in intelligent software, and tech leaders need new insights on the biggest challenges we face in data and AI, as well as the most promising ways to solve them,” said Rachel Roumeliotis, VP of data and AI content at O’Reilly. “O’Reilly Radar will bring together the best minds in data and AI to offer new perspectives, fresh thinking, and practical learnings to help organizations make better decisions about technology, strategy, mission, and purpose.”

Keynotes and sessions include:


  • AI in Healthcare
    • Speaker: Jeremy Howard, Founding Researcher, fast.ai
  • How to Keep Up with ML
    • Speaker: Aurélien Géron, Author of Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

Data Track

  • Prototype to Pipeline: Evolving from Data Exploration to Automated Data Processing
    • Speaker: Sev Leonard, Senior Software Engineer, Fletch
  • Watch Me Learn: Querying Data the Right Way
    • Speaker: Vinoo Ganesh, Head of Business Engineering at Ashler Capital, Citadel
  • Improve Data Quality with a Focus on Data Reliability and Observability
    • Speaker: Barr Moses, Cofounder and CEO, Monte Carlo
  • Train and Predict with Amazon Redshift ML Using SQL
    • Speakers: Chris Fregly, Developer Advocate for AI and Machine Learning, Amazon Web Services, and Antje Barth, Senior Developer Advocate for AI and Machine Learning, Amazon Web Services

AI Track

  • What’s Still Missing from the Responsible AI Movement
    • Speaker: Aileen Nielsen, Fellow in Law and Technology, ETH Zurich, and Author of Practical Time Series Analysis and Practical Fairness
  • MLOps from Zero to One
    • Speaker: Noah Gift, Lecturer at UC Davis and Northwestern and Author of Practical MLOps: Operationalizing ML Models
  • NeuralQA: A Usable Library for Question Answering on Large Datasets Using BERT-Based Models
    • Speaker: Victor Dibia, Research Engineer in Machine Learning, Cloudera Fast Forward Labs
  • Demystifying Scalable Machine Learning with the Spark Ecosystem
    • Speaker: Adi Polak, Senior Software Engineer and Developer, Microsoft, and Author of the upcoming book Machine Learning with Apache Spark

For those located in the Asia-Pacific region, the event will be rebroadcast from 2:00pm to 5:30pm AEDT on Thursday, October 21.

To register for O’Reilly Radar: Data & AI, visit https://www.oreilly.com/online-learning/radar-event-data-ai-2021.html.

About O’Reilly

For 40 years, O’Reilly has provided technology and business training, knowledge, and insight to help companies succeed. Our unique network of experts and innovators share their knowledge and expertise through the company’s SaaS-based training and learning solution, O’Reilly online learning. O’Reilly delivers highly topical and comprehensive technology and business learning solutions to millions of users across enterprise, consumer, and university channels. For more information, visit www.oreilly.com.

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