AI_dev: Open Source GenAI & ML Summit Europe 2025

In this session, we will cover the state of open video generation models. For the past few years, the GenAI community saw an emergence in photorealistic image generation models like Flux, Imagen3, DALL-E 3. 2025 is gradually setting itself up for videos. 

AI models are getting tasked to do increasingly complex and industry specific tasks where different retrieval approaches provide distinct advantages in accuracy, explainability, and cost to execute. GraphRAG retrieval models have become a powerful tool to solve domain specific problems where answers require logical reasoning and correlation that can be aided by graph relationships and proximity algorithms. We will demonstrate how an agent architecture running on a fully open source platform and utilizing GraphRAG retrieval patterns can bridge the gap in data analysis, strategic planning, and retrieval to solve complex domain specific problems.

This lightning talk introduces a practical and visionary approach to building scalable agentic AI systems using Google’s new ADK SDK and deploying them on Kubernetes. Attendees will learn how to design autonomous AI agents with dynamic workflows, hybrid memory (BM25 + vector), and real-time decision-making capabilities. Through a live code walkthrough, I’ll demonstrate how to rapidly build a modular agent using ADK and scale it using cloud-native infrastructure patterns. This talk is ideal for AI engineers, DevOps professionals, and open source contributors looking to bring production-grade agentic intelligence to their systems.

Discover how Back Market uses a Python GenAI application to automate the identification of Personally Identifiable Information (PII) in GitHub.

In the rapidly evolving landscape of artificial intelligence, deploying AI models consistently and securely remains a significant challenge. Ramalama addresses this by merging the robustness of containerization with the intricacies of AI workloads. By default, Ramalama runs AI models in isolated environments, enhancing security and ensuring consistent performance across various platforms. This session will delve into Ramalama's architecture, demonstrating how it simplifies AI deployments, abstracts underlying complexities, and promotes scalability. Attendees will gain insights into integrating Ramalama into their workflows, making AI deployment a straightforward and 'boring' - yet highly efficient - process.

In this presentation, we would like to introduce our open-source AI project: ‍Mozilla.ai Blueprints.

In this talk, I will present a systematic, open source framework for evaluating Gen AI agents—LLM-based systems that manage complex, multi-step tasks—by dissecting their performance into three critical dimensions.

This talk explores groundbreaking methods for enhancing cross-platform AI workloads within container ecosystems. The focus will be on integrating the WebGPU standard into containers, enabling them to utilize host GPUs and other AI accelerators through a flexible API. This approach eliminates the need for creating container images specific to individual GPU vendors and their proprietary drivers. 

Ryoma democratizes enterprise-grade data analysis by providing an open, extensible AI agent framework that integrates seamlessly with modern data stacks. By enabling natural language querying and intelligent workflows, it empowers organizations of all sizes to build powerful data assistants without heavy engineering overhead. Its adoption by companies like JPMorgan, PayPal, and TikTok showcases its real-world impact, while its open-source nature fosters community innovation, standardization, and collaboration across the AI and data ecosystem.

In this talk, we’ll explore how to leverage Kubernetes as an orchestration layer for MLOps workflows powered by MLflow. We’ll show how to combine the flexibility of cloud-native control with the modular capabilities of MLflow to build scalable, reproducible, and secure pipelines. From container management to experiment tracking and model deployment, we’ll walk through a practical strategy to build a modern MLOps environment that balances autonomy with governance. Real-world examples and field-tested best practices are included.