AI Infrastructure Experts Offer Best Practices and Insights for Implementers Attending GTC 2018

by Tony Paikeday
As the world’s AI experts converge on the GPU Technology Conference, organizations from every industry are wrestling with how they’re going to jumpstart efforts to infuse deep learning into some of their most important initiatives.

These endeavors could mean the difference between surviving and thriving in a turbulent market, finding the next wonder-drug, or defending against the next generation of cyber-based threats.

Underpinning these existential challenges and opportunities is a common goal: building an enterprise-grade AI infrastructure that offers disruptive levels of never-before-seen performance.

This year at GTC, AI experts have the opportunity to tap insights that will further their teams’ imperatives like never before. Some common themes you’ll find will include:

1) New innovations that break the speed of scale barrier

As developers try to tackle increasingly more complex neural net models, and embrace model parallelism on a larger scale, implementers will be looking for more efficient ways to not only achieve scale, but speed of scale, with greater ease and less architectural complexity.

2) From fast prototyping to production AI: the workflow impact of GPU workstations

GPU workstations are having a democratizing effect on deep learning workflows, enabling easier experimentation at the desk with frameworks, models and datasets vs. wrestling with IT to get time on a server or renting time in the cloud and worrying about cost-per-training-run.

3) Simplifying AI infrastructure in the data center

DGX simplifies deep learning deployment by systemizing the solution stack inclusive of everything from industry-leading GPUs to performance-optimized frameworks. As teams scale out their environments, important considerations around storage and networking arise.

  • Attend the session “High-Performance Input Pipelines for Scalable Deep Learning” to learn from Brian Gold, R&D director at Pure Storage, on how designing your AI infrastructure with the optimal combination of GPU computing and high-performance storage can accelerate time to solution and ensure predictable performance as your environment scales.

4) Implementer perspectives offer a quicker path to AI success

The surest way to shorten deployment timeframes and increase deep learning return on investment is to hear the pitfalls and best practices of those whose business depends on their AI infrastructure. This panel brings together implementers from a variety of industries, sharing their experiences.

Check out these and the broader suite of DGX-focused sessions at GTC to design, build and scale your AI infrastructure for success. And if you haven’t yet, register for GTC today.

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