MunichTech EXPO – AI, Startups & Innovation

AI agents demo well. Running them in financial services is another story. Join leaders from leading financial institutions, hosted by Diagrid, for candid talks on taking agents from prototype to production, where reliability, security, and regulatory requirements can't be afterthoughts.
What you will learn:
Learn how leading financial services teams make AI agents reliable and secure for production by pairing durable execution (workflows that survive crashes, retries, and restarts without losing state) with verifiable execution (tamper-proof agent history and cryptographic attestation).
Who Should Attend:

• Software architects and engineering leaders at banks, insurers, payments, and fintechs.
• Platform, security, and cyber teams evaluating how to safely deploy AI agents.
• AI/ML and backend engineers building agentic workflows and MCP-based tools.
• Developers at traditional financial services institutions, digitals-natives and fintech startups who want production-grade agent infrastructure fast.
• Anyone responsible for compliance, audit, or risk on AI-driven systems.

Agenda:
* 5:30pm~6:00pm: Checkin, Food and networking
* 6:00pm~6:10pm: Welcome/community update
* 6:10pm~8:30pm: Tech talks
* 8:30pm: Q&A, Open discussion

Federated learning development has two major sources of friction: building the workflow correctly and running it efficiently at scale. New users must learn FL-specific concepts, APIs, job layouts, configuration patterns, and validation workflows before they can create a reliable experiment. Experienced users face a different challenge: repeated FL iteration still requires managing candidate jobs, code changes, runs, results, comparisons, and diagnosis.
At the infrastructure layer, scaling FL on shared HPC systems introduces additional complexity around job scheduling, study isolation, containerized execution, GPU allocation, and avoiding designs where orchestration processes unnecessarily occupy accelerator nodes.
This webinar will discuss two areas where NVIDIA FLARE can help reduce these barriers.
First, we will cover Agentic Skills for Federated Workflows. Current FLARE skills package FLARE-specific knowledge into reusable agent workflows, helping users inspect data, convert existing PyTorch code, generate jobs, validate results, and diagnose failures without first mastering the full NVFLARE API surface. We will also discuss the newly added Auto-FL skills, which extend agentic assistance into iterative experiment development by managing candidate jobs, tracking campaign state, comparing outcomes, and guiding follow-up iterations.
Second, we will cover Scaling NVFLARE on HPC Infrastructure. We will discuss how Slurm integration can evolve beyond basic sbatch submission toward a more complete execution model for shared clusters: isolated studies, Apptainer and other container backends, better lifecycle management, and resource patterns that keep GPUs assigned to training work rather than idle orchestration.
Together, these topics show how NVFLARE is evolving across the full path from development to deployment: lowering the learning curve for new users, accelerating iteration for experienced users, and improving large-scale execution on modern HPC systems.

• 1. Agentic Skills for Federated Workflows
• 2. Scaling NVFLARE on HPC Infrastructure

Venue:
virtual, join from anywhere.
Join: https://teams.microsoft.com/meet/231857209302585?p=saScoNOLEmVRzrg255
Meeting ID: 231 857 209 302 585
Passcode: TZ39Kt9w