Domino at NVIDIA GTC

Data Science Innovation at Scale with Enterprise MLOps
March 21 - 24, 2022

Learn from Data Science Leaders


Domino Data Lab’s collaboration with NVIDIA represents the ultimate ML stack for modern enterprises. Newly unveiled support for NVIDIA Fleet Command, LaunchPad and NGC Hub, and on-demand MPI Clusters provides enterprise MLOps benefits of workload orchestration, self-serve infrastructure, and collaboration - paired with cost-effective scale.

This latest collaboration supports even more data science use cases - from cutting edge research teams experimenting in AI training on purpose-built AI hardware to data science teams managing models deployed enterprise-wide in next-gen applications - in data centers or at the edge.

Join Domino at NVIDIA GTC. You’ll hear from data science innovators at enterprises like Allstate, Eli Lilly, McKesson who have put models at the heart of their businesses.

GTC Speakers

Andrea de Souza
Global Head of Data Sciences & Engineering, Eli Lilly
Susan Hoang
Vice President of Oncology Analytics, McKesson
walters_headshot (1)
Meg Walters
Head of Analytics CoE, Allstate
Craig McLuckie
VP R&D, VMware, Kubernetes OSS Cofounder
John Ashley
GM, Financial Services & Technology, NVIDIA
Rima Alameddine
VP, Enterprise Sales, HCLS & Manufacturing, NVIDIA
priya tikoo
Priya Tikoo
Sr. Technical Product Manager, NVIDIA
Chris Yang
CTO & Co-founder, Domino
Nick Elprin
CEO & Co-founder, Domino
Thomas Robinson
VP, Strategic Partnerships and Corp Dev, Domino
Josh Mineroff Headshot_1642125086001001uz3N
Josh Mineroff
Sr. Partner Solutions Architect, Domino

Domino Data Lab GTC Sessions

On-Demand Sessions Available Now

How to Scale Data Science for Innovation Across the Healthcare Value Chain

Can data science increase the likelihood and velocity of scientific discoveries making it into clinical practice? How has COVID-19 accelerated digital transformation, and what innovation in AI/ML has this driven? Digital healthcare data and machine learning at scale has brought unprecedented change in the healthcare and life sciences sector, with data and predictive models driving a new focus on innovation across the value chain.

In this panel discussion moderated by Thomas Robinson, VP of Strategic Partnerships & Initiatives at Domino Data Lab, you’ll learn from healthcare leaders like Andrea Desouza, Global Head of Data Sciences & Engineering at Eli Lilly, Susan Hoang, VP Oncology Intelligence Analytics at McKesson, and Rima Alameddine, VP of Sales for Healthcare and Manufacturing at NVIDIA, as they discuss the journeys they’ve taken in deploying data science at scale. They’ll discuss best practices across people, process, and technology for MLOps at enterprise scale.
How Allstate’s Analytics Center of Excellence Pushes the Innovation Pedal for Model-Driven Business Transformation

Allstate serves as a leader in insurance by putting models at the heart of its business, leveraging data as a transformational enterprise asset. Using data and analytics to support claims processing, help deliver quotas, and guide thousands of decision-making actions across products, sales, operations, marketing, and claims, Allstate leads the industry in applying models and AI to critical business problems. At the core of this transformation is Allstate’s Analytics Center of Excellence, led by Meg Walters.

In this panel discussion, Meg, alongside Nick Elprin, CEO of Domino Data Lab, and John Ashley, GM of Finserve & Tech at NVIDIA, discuss how Allstate has scaled the Analytics CoE into a model building factory using Domino’s Enterprise MLOps platform to deliver massive productivity gains for data scientists. Learn how Allstate creates an innovative culture around the latest data science techniques such as NLP, computer vision, weather, autonomous vehicles, & multi-modal modeling.
A Vision for Kubernetes as the Foundation for Enterprise MLOps

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 challenges in scaling data science - and how virtualized, containerized data science workloads set the foundation for AI adoption in the enterprise.
Virtualize GPU-accelerated Data Science and AI Workflows in Your Data Center with Enterprise MLOps [S41867]

GPU acceleration brings great promise to data science and AI workloads, but not without challenges. While MLOps can bring the benefits of collaboration and self-service infrastructure to data science teams and AI practitioners, the complexity of integrating AI workloads with existing infrastructure is frequently cited as a top barrier to AI adoption and business impact.

Combining Domino Data Lab and NVIDIA-accelerated computing enables companies to cost-effectively scale data science by accelerating research, model development, and model deployment on mainstream accelerated servers. Data scientists can focus on research instead of DevOps by launching environments on demand, with docker images configured with the latest data science tools, frameworks, and optimized accelerated compute — with automatic storing and versioning of code, data, and results. IT can have the confidence of enterprise-grade security, manageability, and support within a familiar environment. Learn how to cost-effectively scale data science!

See our on-demand sessions