Autonomy
On-Device ML
Enterprise Use Case
Industrial Edge
Running workloads on the edge means operating with a finite amount of compute resources, which means that running workloads as lightweight as possible is generally preferable. In contrast, securely operating workloads in the cloud often means spinning up additional virtual machines for strong workload isolation. On the edge, this strategy is often not feasible. This talk covers how you can securely run containers in lightweight Kubernetes clusters on the edge entirely without the need for virtualization. This talk will cover different hardening approaches and compare them with special focus on securing AI workloads on the edge.
Autonomy
On-Device ML
Compute
Industrial Edge
Lukas Gentele
CEO
vCluster Labs
Lukas Gentele is the CEO & Co-Founder of vCluster Labs, a venture-backed startup virtualizing Kubernetes to enable advanced tenancy models to increase utilization, reduce cost and make Kubernetes more dynamic than ever. vCluster enables platform and infra teams to create virtual Kubernetes clusters which are as scalable and isolated as traditional clusters but much more lightweight and versatile. Trusted by companies such as CoreWeave, Nscale, Adobe, and Deloitte, vCluster enables organizations to create fully isolated tenant clusters on any infrastructure, from public cloud to private clouds all the way to AI supercomputers powered by the latest GPUs. Lukas has been a keynote speaker and presenter at events such as KubeCon, DevOpsDays, ContainerConf, as well as at AWS and Kubernetes Community Days around the world.
Autonomy
Robotics
On-Device ML
Enterprise Use Case
Industrial Edge
Panelists
Joji Philip
Director, AI & ML Products
Ericsson
Autonomy
Robotics
On-Device ML
Enterprise Use Case
Eliana Stefani
Staff Autonomy & AI Research Engineer
Lockheed Martin
Eliana Stefani, PhD is a Staff Autonomy & AI Research Engineer at Lockheed Martin, with expertise in autonomous systems, AI, and robotics. She has experience architecting solutions to complex problems, leading teams across engineering disciplines, and deploying artificially intelligent systems to the edge.
As Principal Investigator, she architected the autonomy stack and developed novel autonomous driving software for the Lunar Terrain Vehicle, enabling a rover to navigate dynamic obstacles while traversing unmapped environments; garnering interest from additional customers.
As Program Manager and later Chief Engineer, she architected system design, led a multi-disciplinary engineering team from research concept to successful test events, guided the program to a contract win, and interfaced with customers and stakeholders.
Dr. Stefani's strong track record of delivering high-impact results and driving program success has been recognized with multiple awards from within Lockheed Martin. She has presented at conferences including NVIDIA's GTC-DC and Amazon's re:MARS.
Autonomy
Robotics
On-Device ML
Enterprise Use Case
Bala Chidambaram
Chief Engineer & Senior Director, Autonomy, Office of the CTO
Boeing
On-Device ML
Compute
Matt Roman
Senior Director, Design Engineering
Celestica
Marc Austin
Founder & CEO
Hedgehog
Florian Berchtold
Principal Software Engineer
Zipline
Industrial Edge
On-Device ML
Compute
Aniruddha Maru
VP, Infrastructure
Standard AI
Maru is a seasoned DevOps engineer with over 15 years of experience architecting solutions that scale. He currently leads the Infrastructure team at Standard AI, where he orchestrates hybrid infrastructure that powers cutting-edge machine-learning workloads that are reshaping retail.
After earning bachelor’s and master’s degrees in Computer Science from Indian Institute of Technology (IIT) Bombay, Maru has conquered diverse tech stacks throughout his career - from desktop and mobile applications to large scale distributed systems. Over his decade-plus journey through the startup ecosystem, he's delivered breakthrough solutions including smart car IoT devices, intelligent music learning platforms, and autonomous checkout systems. Maru gravitated towards the DevOps culture, finding a passion for scalable cloud-native technologies and continuous delivery of AI workloads.
The generative AI revolution presents a dilemma: the best models are in the cloud, but your data is on-prem due to sovereignty, security, or latency needs. This makes on-prem AI complex and costly. This session explores how Google Distributed Cloud (GDC) solves this by bringing a managed platform with Google's best AI—including Gemini and Vertex—to your environment. Learn how GDC's flexible connected and air-gapped solutions are empowering defense, finance, and other critical industries to securely unlock their data.
Industrial Edge
On-Device ML
Optimized Infrastructure
Andrew Fetterer
Group Product Manager
Google Cloud
A seasoned product leader who is passionate about cloud and innovation , Andrew Fetterer serves as the Group Product Manager for Google Distributed Cloud. In this role, he is responsible for the outbound product management that shapes how Google Cloud infrastructure operates on-premises and at the edge for the world's leading organizations.
Throughout his career, Andrew has a proven track record of building and scaling platforms and infrastructure from concept to category leadership.
Industrial Edge
Enterprise Use Case
Prashanth Chakravartula
Senior Director, Warehouse & Logistics
Bayer
Industrial Edge
On-Device ML
Gen AI
Manuel Botija
VP, Product Management
Axelera
Manuel Botija is an engineer with degrees from Telecom Paris and Universidad Politécnica de Madrid. Over the past 17 years, he has led product innovation in semiconductor startups across Silicon Valley and Europe. Before joining Axelera, Manuel served as Head of Product at GrAI Matter Labs, which was acquired by Snap Inc.