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From Blind Spots to Breakthroughs: Real-Time AI Factory Observability that Cuts Costs and Boosts Performance

Your AI infrastructure is only as effective as your visibility into it, and right now, most teams are flying blind. In this hands-on workshop, you’ll learn how to use real-time observability to reduce costs, eliminate waste, and keep your AI Factory running at peak performance. We’ll dive into practical techniques to:

Identify GPU underutilization, throttling, and idle capacity across both cloud and on-premises deployments before they burn through your budget.
Monitor token usage for inference workloads (including NVIDIA NIM containers) to catch cost spikes and inefficiencies as they happen.
Correlate slow inference jobs or degraded model performance to root-cause issues anywhere in the stack, so you can fix problems without throwing more hardware or cloud spend at them.

Through live demonstrations, you’ll see how real-time telemetry and AI-driven correlation turn raw metrics into immediate, actionable insights, helping you cut unnecessary spend, speed up troubleshooting, and ensure your models deliver maximum value. If you’re responsible for making AI infrastructure faster, leaner, and more cost-efficient, this is the one workshop you can’t afford to miss.

Location: Room 201

Duration: 1 hour

Author:

Devin Avery

Product Development Architect

Virtana

Devin Avery brings over 20 years of experience in software engineering, specializing in enterprise and service provider software development. Currently serving as Product Development Architect at Virtana, he is driving the company’s approach for AI Factory Observability, Generative AI capabilities, and Infrastructure Observability. His career includes previous principal software engineering roles at Brocade and software engineering roles at CA Technologies, establishing a solid foundation in developing complex enterprise solutions.

Devin has a proven track record of designing and delivering scalable, high-quality software solutions through a disciplined, iterative, and use-case-driven design and testing philosophy. He holds multiple patents for his work on algorithms related to applying collection policies, traversing topologies, and testing abstractions. With a Bachelor of Science in Computer Science from the University of New Hampshire, Devin has a keen ability to decompose high-level user requirements into executable stories and effectively bridge the gap between development teams and product management. His skills have been instrumental in transforming legacy products into modern, customer-centric solutions.

Author:

Meeta Lalwani

Senior Director of Product Management

Virtana

Meeta Lalwani is Senior Director of Product Management at Virtana, where she leads the company’s AIOps, Generative AI, and Event Intelligence portfolio for its SaaS-based observability platform. With over 18 years of experience in enterprise software, she has a track record of delivering products that solve complex infrastructure challenges at scale.

At Virtana, Meeta drives the vision and execution for AI Factory Observability, enabling organizations to monitor, troubleshoot, and optimize performance across GPUs, compute, network, storage, and AI workloads. Her expertise spans full-stack observability, large-scale distributed systems, and applying AI/ML to improve system reliability and efficiency.

Meeta is known for bridging deep technical insight with practical business outcomes, helping teams reduce costs, increase performance, and accelerate innovation in demanding hybrid and AI-first environments.