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Date: January 15, 2019 Time: 10:00am PT/1:00pm ET

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Add to Calendar 01/15/2019 10:00 AM 01/15/2019 11:00 AM America/Los_Angeles Cloud-Native Machine Learning and Data Engineering: Emerging Capabilities and the Road Ahead Data platforms are being asked to support an ever-increasing range of workloads and compute environments, including scale-out machine learning and elastic container platforms such as Kubernetes. In this webinar, we will share emerging capabilities in the industry for running large-scale machine learning and data engineering workloads in cloud-native environments. We’ll also share our vision of the road ahead for machine learning and data engineering in the cloud. Online Webinar

Containers and the Kubernetes ecosystem are bringing the agility of cloud everywhere, providing a consistent user experience for applications deployed across hybrid and multi-cloud environments.  These technologies also offer new opportunities to unify data engineering and machine learning workflows and make it easier and faster to build, train and deploy models consistently, at scale.

In this webinar, we introduce Cloudera Machine Learning, Cloudera’s new cloud-native machine learning platform powered by Kubernetes that makes it easy to quickly provision environments and scale resources for end-to-end data engineering and machine learning workflows, with secure data access and a unified experience across on-premises, public and hybrid cloud environments.

Learn more about:
  • Fast cloud provisioning and autoscaling for improved efficiency and agility

  • Containerized Python, R, and Spark-on-Kubernetes for scale-out data engineering and machine learning with seamless dependency management and autoscaling

  • Distributed GPU training for accelerated deep learning


CTO for Machine Learning

Tristan Zajonc


Tristan Zajonc is CTO for Machine Learning at Cloudera. Tristan previously led engineering for Cloudera Data Science Workbench and was the co-founder and CEO of Sense, an enterprise data science platform that was acquired by Cloudera in 2016. He has over 15 years experience in applied data science, machine learning, and machine learning systems development across academia and industry and holds a Ph.D. from Harvard University.

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