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Everyone is talking about machine learning (ML) and the benefits of AI for advancing the detection of cyber threats.  And for very good reasons. The benefits are real. 

What can these algorithms and methods actually do in cybersecurity? How can you build an ML strategy to move from the lab and deploy it into the security operations center? Many models are available to spot the hidden threats or improve on traditional rules in more dynamic and relevant in changing environments.  But ML is about more than just new behavior models for detection.

In this webinar, we’ll cover how to:
  • Embed ML in processes to augment people with timely, actionable insights so they can focus on the right risks while machines do the busy-work

  • Transition ML models from the lab and into production quickly to meet fast-evolving threat and discoveries

  • Address challenges related to limited or poorly categorized data

This webinar is essential for stakeholders grappling with how to move ML technology from testing to deployment to keep their business secure.



Senior Director, Product Management - Cybersecurity Solutions

Simon Elliston Ball


Simon is the product manager for cyber security solutions at Cloudera. His background is in applying data science to streaming data sources, helping people make sense of big data and streaming data building tools to help developers work with data. In the past he has worked in the data intensive worlds of hedge funds, retail software vendors and large scale network and compute infrastructure management. All this comes together into a data science driven approach to cyber security and a realistic approach to machine learning. He lives in Cambridge, where he mainly debates locals in pubs about machine learning and board games.

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