A Vision for Kubernetes as the Foundation for Enterprise MLOps

Learn from innovators in Kubernetes and GPU-accelerated data science
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Kubernetes, an open-source container orchestration system, is becoming the consensus API for infrastructure for IT professionals. For data scientists, the once onerous task of environment and package management is made tremendously easier by containers. And Kubernetes brings a whole new set of benefits for data scientists, including making models portable and reproducible, handling bursty compute requirements of AI workfloads, and future-proofing infrastructure.

In this panel discussion moderated by Chris Yang, CTO and co-founder of Domino Data Lab, Craig McLuckie, VP of R&D at VMware and Kubernetes Project Co-Founder, and Chris Lamb, Vice President of GPU Computing Platforms at NVIDIA, will discuss:

  1. The history of Kubernetes, and why it has risen to prominence in IT infrastructure management.
  2. Challenges in scaling data science, and foundational architectural decisions.
  3. How virtualized, containerized data science workloads set the foundation for AI adoption in the enterprise.

Craig McLuckie

VICE PRESIDENT OF R&D, VMWARE & KUBERNETES PROJECT CO-FOUNDER, VMWARE

Craig is an entrepreneur and innovator in cloud and enterprise software, passionate about reducing the complexity of building and operating IT systems. He's currently a VP of R&D at VMware, leading the engineering, product management, and site reliability engineering team for Tanzu.

Chris Yang

CTO AND CO-FOUNDER, DOMINO DATA LAB

Chris Yang is CTO and co-founder of Domino Data Lab. His focus is exploring and delivering innovative product ideas that can accelerate our customers' delivery of ML models. Before founding Domino, Chris worked at Bridgewater, one of the world’s largest hedge funds, working directly with senior research leadership to prototype and deliver their next-generation investment platform.

Chris Lamb

VICE PRESIDENT, GPU COMPUTING SOFTWARE PLATFORMS, NVIDIA

Christopher runs a worldwide team building platforms for parallel high-performance computing and AI/deep learning across HPC, enterprise data center, cloud, edge/internet of things, embedded, and automotive. He works on the world's most powerful supercomputers to design the smartest self-driving cars, and everything in-between.

Watch On Demand

Kubernetes in Data Science

You’ll hear directly from innovators on the history of Kubernetes and why it is essential for scaling data science across the enterprise.

Foundational Decisions for Enterprise Scale

Learn how you can avoid applying an "on-prem mentality" to managing kubernetes clusters, as well as the foundational architectural decisions that must be made for enterprise-wide AI adoption.