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Cloudera Cloudera

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The success of data engineering and data science projects often hinges on the ability for platforms to curate and process massively growing amounts of data. While CPU-based computing frameworks are useful for serial workloads, delivering results from large, complex big data continues to be a challenge for enterprise organizations.

For modern data workflows, data science teams need a robust platform that facilitates collaboration and accelerates their time to insight. In this session you will learn:

  • How Cloudera Data Platform (CDP), supercharged with NVIDIA GPUs, enables the full data lifecycle of data from ingest, preparation, analytics to machine learning and applications.

  • The best practices and approaches to using GPUs across both public, private, and hybrid cloud deployments

  • How NVIDIA GPUs on CDP enables a fast and robust computation backbone through the entire ML workflow.

 

 

Speakers

Director of Product Marketing, Data Engineering & Machine Learning

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.

Principal Product Architect

William Benton

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William Benton works in the NVIDIA data science product group and is passionate about making it easy for machine learning practitioners to benefit from advanced infrastructure and making it possible for organizations to manage machine learning systems. In previous roles, he has defined product strategy and professional services offerings related to data science and machine learning, led teams of data scientists and engineers, and contributed to open source communities related to data, ML, and distributed systems.

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