Open Source Summit North America 2025

Edge AI today relies on centralizing data from edge devices to the cloud, but this is impractical due to costs and privacy constraints. Federated Learning (FL) is a viable alternative: edge nodes collaboratively train a ML model without transferring or exposing proprietary data. Instead, only model weights are shared, allowing each entity to develop a model that outperforms what it could train independently. Despite its potential, FL is largely academic due to the complexity of integrating expertise across the technology stack. Additionally, decentralized data can be heterogeneous, requiring non-generalizable, application-specific solutions. EdgeLake-FL is a hardware-agnostic framework leveraging EdgeLake, an LF Edge project, to automate the continuous learning FL workflow. With EdgeLake as the data management layer, decentralized data appears centralized and data heterogeneity is resolved. Using EdgeLake-FL, an ML engineer publishes a training application, and Edge nodes with relevant data autonomously train, share, and aggregate models. Each node can then leverage the aggregated models for inference directly at the Edge. In this talk, I will demo EdgeLake-FL in a real use case.

Enterprises struggle to integrate fragmented generative AI (GenAI) technologies. Due to its rapid evolution and diverse implementations, even top LLMs hallucinate when answering Kubernetes-related questions.

The Open Platform for Enterprise AI (OPEA), a Linux Foundation project, accelerates GenAI adoption with an orchestration framework that composes microservices via customizable blueprints to deploy or create GenAI applications.

In this hands-on tutorial, developers will deploy advanced retrieval-augmented generation (RAG) applications using Kubernetes. They’ll explore how OPEA orchestrates AI workloads via a microservices architecture and build a production-ready RAG chatbot. Attendees will go beyond deployment, enhancing vector search with knowledge graphs, customizing OPEA components, and scaling AI solutions efficiently on Kubernetes—all while integrating AI agents for more intelligent automation.

Organizations face a critical challenge: data is growing exponentially across distributed locations, but traditional centralized processing approaches are becoming unsustainable. With the majority of enterprise data going unused, companies struggle with massive transfer costs, compliance issues, and network reliability problems when moving data to centralized infrastructure.

This talk introduces a paradigm shift: bringing compute to where data lives. Using the open-source Bacalhau project, we'll demonstrate how to:

- Deploy distributed processing jobs across clouds, edge devices, and on-premises infrastructure
- Reduce data movement costs while maintaining centralized control
- Ensure compliance by processing sensitive data in place
- Enable real-time analytics at the edge

Through real-world examples, including an energy company managing 15,000 microgrids and cities processing camera feeds, attendees will learn practical patterns for modernizing their data infrastructure. We'll explore architectural patterns, security considerations, and best practices for implementing compute-over-data architectures.

AI Agents have become a buzzword in the world of generative AI—but what exactly are agents, and how are they advancing open source AI? Agents are autonomous systems that extend the capabilities of Large Language Models (LLMs) by leveraging external tools and real-time data to perform dynamic actions for solving complex, multi-step tasks. They are at the forefront of transforming open source AI by enabling adaptability, automation and more intelligent decision-making.
In this talk, we’ll explore popular open source agent frameworks like LangChain and Haystack, which are driving rapid development and community-powered innovation in AI agents. Using real-world examples and a live demo, we’ll demonstrate how these frameworks can be leveraged to build a variety of generative AI applications, including RAG, IT operations automation and dynamic, context-aware chatbots.
Attendees will gain a clear insight into why AI agents are at the forefront of innovation and how open source collaboration is driving its evolution. Whether you’re an AI developer, OSPO leader or open source enthusiast, this session will highlight the potential of agents to power the next wave of open source AI missions.