Welcome to the AI meetup in Silicon Valley, in collaboration with Rhino and NVIDIA. Join us for deep dive tech talks on AI, GenAI, LLMs and Agent, hands-on experiences on code labs, workshops, and networking with speakers and fellow developers.

Agenda:
- 5:30pm~6:00pm: Checkin, food and networking
- 6:00pm~6:10pm: Welcome, Community update
- 6:10pm~8:00pm: Tech talks and Q&A
- 8:00pm~8:30pm: Open discussion, Mixer and Closing.

Tech Talk: Operationalizing Federated Learning: Deploying, Scaling, and Integrating Models, Agents, and Apps.
Speaker: Adrish Sannyasi, Vice President, Customer Solutions and Delivery, Rhino Federated Computing Platform
Abstract: Federated learning enables collaborative model training across distributed data sources without centralizing sensitive data, making it an attractive approach for regulated and data-intensive industries. However, real-world deployments introduce challenges that go far beyond research prototypes, including heterogeneous infrastructure, data imbalance, data source connectivity, security, governance, and operational scalability.
This interactive lunch & learn focuses on federated learning as deployed in production systems, emphasizing industry-driven use cases such as drug discovery, federated clinical data networks, financial fraud detection, cybersecurity, and automotive systems, among others. Using the Rhino Federated Computing Platform (FCP) and NVIDIA accelerated infrastructure and NVIDIA FLARE framework software, the session examines how federated learning workflows are implemented across cloud and on-prem/edge environments.
Through concrete deployment examples and a guided user workflow and technical walkthrough, attendees will explore how federated learning jobs are orchestrated across heterogeneous multi-cloud systems, how aggregation and policy enforcement rules are applied, and how real-world issues such as security, compliance, and system scaling are handled. The session will also highlight lessons learned from moving federated learning systems from pilot phases into production in multiple industries, including infrastructure setup, FL data contribution cycle, data contribution incentive management, system performance optimization, and model and data lifecycle management.
Additionally, the session extends beyond model generation to demonstrate the complete production lifecycle. We will examine the transition from collaborative training to decentralized inference and decentralized agents, illustrating how a global model—such as a fraud detection system or a molecular property prediction model trained across multiple banks/pharma companies—is deployed back to local environments to score live data. We will then discuss the "last mile" of FL: integrating these inference signals into downstream applications.

Tech Talk: Building Production Federated AI with NVIDIA FLARE: APIs, Operations, Agents, and Enterprise Scale
Speaker: Chester Chen, Senior Product & Engineering Manager, NVIDIA Federated learning, FLARE | Peter Cnudde, Director of Engineering, NVIDIA Federated learning, FLARE,
Abstract: This session explores how NVIDIA FLARE supports the full lifecycle of production federated AI, from application development to deployment, operations, automation, and future agent-assisted workflows. We will begin with the modern FLARE API direction, including the Client API for converting existing ML code into federated workloads, the Recipe API for reusable job construction, and the FLARE API/CLI for programmatic job submission, monitoring, and system operations.
We will highlight key FLARE 2.8.0 capabilities that make real-world deployments easier to operate: modern NVIDIA FLARE CLI automation with JSON/schema support, docker and Kubernetes deployment preparation, Docker/Kubernetes job launchers, multi-study support, live log streaming, tensor disk offload for large-model FedAvg, improved large-model streaming reliability, and new examples for multimodal, language-model, financial-services, and privacy-oriented workflows.
The session will also look ahead to where FLARE is heading: Collaborative API support for more flexible federated algorithms and analytics, FLARE agentic readiness with skills and benchmarks, Auto-FL research loops, Slurm/HPC support, memory-management enhancements, and integration with enterprise applications. Attendees will leave with a practical understanding of how FLARE is evolving into a production platform for secure, scalable, and increasingly automated federated AI across cloud, on-prem, edge, and HPC environments.

Speakers/Topics:
Stay tuned as we are updating speakers and schedules. If you have a keen interest in speaking to our community, we invite you to submit topics for consideration: Submit Topics

Venue:
SVAI Hub, 135 Constitution Drive, Menlo Park, CA 94025

Sponsors:
We are actively seeking sponsors to support AI developers community.  Whether it is by offering venue spaces, providing food, or cash sponsorship. Sponsors will not only speak at the meetups, receive prominent recognition, but also gain exposure to our extensive membership base of 30,000+ AI developers in San Francisco Bay Area or 350K+ worldwide.

Local and Global AI Community on Discord
Join us on discord for local and global AI tech community:
- Events chat: chat and connect with speakers and global and local attendees;
- Learning AI: events, learning materials, study groups;
- Startups: innovation, projects collaborations, founders/co-founders;
- Jobs and Careers: job openings, post resumes, hiring managers;