Join our in-person meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision.

As deep learning models grow in complexity, particularly with the rise of Large Language Models (LLMs) and generative AI, scalable and cost-effective training has become a critical challenge. This talk introduces Ray Train, an open-source, production-ready library built for seamless distributed deep learning. We will explore its architecture, advanced resource scheduling, and intuitive APIs that simplify integration with popular frameworks such as PyTorch, Lightning, and HuggingFace. Attendees will leave with a clear understanding of how Ray Train accelerates large-scale model training while ensuring reliability and efficiency in production environments.

As enterprises move from GenAI prototypes to in-production applications, standalone vector databases often fall short on synchronization, ACID compliance, and resilience. This session demonstrates how PostgreSQL-compatible distributed SQL databases address these challenges while maintaining a familiar developer experience. We’ll cover scaling RAG architectures with pgvector across regions, multi-agent patterns. Attendees will learn how to achieve ultra-resilience for peak traffic, grey failures, and disasters, along with key design principles such as unified data sources, open standards, and multi-tenant security. Engineers and architects will leave with practical strategies for building globally scalable, enterprise-grade GenAI applications.

World Models are emerging as the defining paradigm for the next decade of robotics and autonomous systems. Instead of depending on handcrafted perception stacks or rigid planning pipelines, modern world models learn a unified representation of an environment—geometry, dynamics, semantics, and agent behavior—and use that understanding to predict, plan, and act. This talk will break down why the field is shifting toward these holistic models, what new capabilities they unlock, and how they are already transforming AV and robotics research.

We then connect these advances to the Physical AI Workbench, a practical foundation for teams who want to build, validate, and iterate on world-model-driven pipelines. The Workbench standardizes data quality, reconstruction, and enrichment workflows so that teams can trust their sensor data, generate high-fidelity world representations, and feed consistent inputs into next-generation predictive and generative models. Together, world models and the Physical AI Workbench represent a new, more scalable path forward—one where robots and AVs can learn, simulate, and reason about the world through shared, high-quality physical context.

During this presentation we demostrate efficient uses of reasoning to automate data-flywheels towards continuous model improvement