AI Innovators Munich with Weights & Biases × AppliedAI

• A practical, no-nonsense guide to AI governance and EU AI Act readiness – what technical leaders actually need to implement, not just what the regulation says
• A blueprint for moving beyond isolated pilots and into production-grade AI systems that scale reliably across teams and the wider organisation
• How robotics organisations are operationalising AI today, and the lessons enterprise teams can take directly back to their own platforms
• Networking over drinks with Munich’s senior AI and ML engineering community

• Governance as the Foundation: how to translate EU AI Act requirements into real engineering workflows, embedding compliance into development from day one and building trust through transparency, evaluation, and auditability.
• The AI Control Plane: obtaining an organisational, single layer of visibility and control across every model, application, or AI agent, by monitoring performance, running evaluations, and driving continuous improvement throughout their full lifecycle.
• From POC to Production at Scale: the evaluation pipelines, observability practices, infrastructure patterns, and organisational readiness needed to make AI repeatable and reliable across teams.
• Lessons from Physical AI & Robotics: why robotics teams are ahead in operational AI, and how enterprise teams can apply their approach to managing real-world complexity, robust evaluation, and accountable AI systems.

• Senior AI & Data Science Leaders
• ML Engineers, AI Researchers & MLOps Practitioners
• Robotics & Physical AI Teams
• Platform & Infrastructure
• AI Product Leaders & Managers
• Anyone building, scaling, and operationalising production AI systems, aligning governance and compliance or accountable for AI strategy and outcomes

From Physics to Intelligence: Scaling Robotics & Physical AI

Robotics and physical AI systems introduce a new level of complexity to the ML lifecycle — combining simulation, real-world data, hardware constraints, and large-scale experimentation.

In this session, we’ll explore:

• Managing experimentation across simulation and real-world robotics environments
• Tracking multimodal data (vision, sensor, control signals) at scale
• Evaluating embodied AI systems beyond traditional ML metrics
• Building reproducible workflows for robotics and physical AI teams

We’ll share practical lessons from teams developing next-generation intelligent systems, and how modern MLOps tooling accelerates iteration from physics to deployed intelligence.