Name: Distributed Training at Scale with NetApp & Run:ai
Start: 2022-04-29T18:10:00Z
End: 2022-04-29T18:10:56.000Z
Location: BrightTALK
Rating: 0

Run:ai’s Atlas platform provides a ‘Foundation for AI Clouds’, whether on premises, across public clouds, or at the edge, allowing organizations to have their AI resources on a single, unified platform that supports AI at all stages of development, from building and training models to running inference in production. Organizations using Run:ai increase resource utilization by an average of 2x. Customers include Fortune 500 companies and cutting-edge AI startups from multiple verticals like finance, automotive, healthcare, and gaming, as well as leading academic AI research centers.

What’s the “right” way to build your AI infrastructure stack? Today, GPU infrastructure for building and training models is mostly built on bare metal using static resource allocation, a recipe for wasted resources and lost time. Hear from Gijsbert Janssen van Doorn, Director of Technical Product Marketing at Run:ai about how Run:ai Atlas together with VMware Tanzu can help you build a cloud-native AI platform that delivers value and ROI all the way from model build right through to deployment.

Learn about:
• Smarter workload scheduling with Kubernetes
• AI orchestration concepts borrowed from the world of HPC to better manage expensive resources
• Autoscaling, from fractional GPUs to multiple nodes of GPUs for distributed training using batch workloads
• Productizing AI—taking models into production with ease ensuring tight SLAs are met

Build Production-Ready AI Infrastructure

In this week's Beers with Engineers, we show how virtualization can supercharge your AI workloads and how it links to your abstraction layer. We also introduce rntop, an open-source tool you can use to measure your GPU cluster utilization - the results might surprise you! Hosted by Gijsbert Janssen van Doorn, Director Technical Product Marketing & Raz Rotenberg, Software Team Lead, and featuring Noa Neria, Senior Software Engineer. Get rntop on GitHub: https://github.com/run-ai/rntop

Beers with Engineers: Supercharge your AI cloud with virtualization

In this latest Data Science Central webinar, meet Shadow IT’s younger sibling. Shadow AI is the result of one-off AI initiatives inside organizations, where siloed AI teams buy their own infrastructure or use cloud compute resources. While no organization wants to stifle the ambition of its data science teams, this decentralized approach results in many AI initiatives never making it to production. Learn from Gijsbert Janssen van Doorn at Run:ai how to centralize AI so that it is accessible and productive across an entire organization.

You’ll learn:
- Why Shadow AI exists, and how to prevent it at your organization
- The benefits of centralizing your AI compute resources
- What a best-practice centralized AI infrastructure looks like

Shadow AI, the Silent Killer of Deep Learning Productivity

In this week's Beers with Engineers, we talk orchestration options with or without Run:ai, examples of success, & why effective orchestration matters for users & platform owners. Hosted by Gijsbert Janssen van Doorn, Director Technical Product Marketing & Natasha Romm, Senior Software Engineer.

Beers with Engineers: Effective cloud AI orchestration strategies

In this week's Beers with Engineers, we overview the "AI Cloud Journey" - How to set up your basic infrastructure & hardware foundations, containers & tools. Hosted by Gijsbert Janssen van Doorn, Director Technical Product Marketing & Ronen Dar, CTO of Run:ai.

Beers with Engineers: Building your first AI cloud

Deep learning models are becoming more ambitious by the day, but their supporting infrastructures often struggle to keep up. Enter distributed training, the solution for training deep learning models and large data sets. In distributed training, storage, compute power and batch size are magnified with each added GPU, dramatically reducing training time. Learn how NetApp, Run:ai, and Microsoft Azure teamed up to tackle the challenges of autonomous vehicle lane detection using distributed training, in this webinar featuring Sam Charrington of TWIML.

Distributed Training at Scale with NetApp & Run:ai

Autonomous Vehicles

Computer Vision

Machine Learning

NetApp

Azure

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Distributed Training at Scale with NetApp & Run:ai

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