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For enterprise businesses, applying advanced machine learning (ML) techniques like deep learning can deliver transformative business outcomes, yet the black-box nature of these approaches creates barriers of understanding that can slow adoption to a halt.  ML model interpretability, or the ability to explain why and how a model makes a prediction, can enable business stakeholders to quickly understand the how and why of predictive outcomes and confidently make decisions that optimize for future business results

JOIN THIS WEBINAR TO:
  • Discover best practices for building and deploying interpretable ML models at scale

  • Learn how Cloudera Machine Learning’s model ops and interactive applications functionalities deliver business-ready predictive apps for business users

  • Explore an in-depth technical guide to ML interpretability and working application prototypes from Cloudera Fast Forward Labs

Speakers


Senior Manager, Product Marketing MLOps

Santiago Giraldo

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Santiago leads product marketing for Cloudera’s production machine learning products. With over 10 years in the data science and analytics software industry, Santiago focuses on enabling businesses to solve complex challenges with novel data strategies and machine learning approaches.

ML Research Engineer, Cloudera Fast Forward Labs

Victor Dibia

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Victor is a Research Scientist/Engineer at Cloudera Fast Forward Labs . He holds a PhD in Information Systems from City University of Hong Kong and an MSc in Information Networking from Carnegie Mellon University. Victor previously worked as a Research Staff Member at IBM Research , as Technical Lead for MIT Global Startup Labs, as a researcher at the Innovation Management Lab, Athens Information Technology, Athens Greece, and IT startup founder/lead developer.

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