Ravi Chandu Ummadisetti, Toyota, Generative AI Architect Kordel France Toyota, AI Architect
Toyota applies the Multi-Agentic framework, powered by Generative AI, to revolutionize car manufacturing, battery production, paint design, and anti-corrosion strategies. In manufacturing, AI-driven agents collaborate to optimize production lines. For battery production, Generative AI designs efficient layouts and predicts performance, enhancing durability and sustainability. In paint design, AI generates custom finishes with superior aesthetics and durability, while simulations ensure precision and reduce waste. Anti-corrosion efforts leverage AI to develop advanced coatings tailored to environmental and material conditions, extending vehicle lifespan and minimizing environmental impact.
Ravi Chandu Bio (Generative AI Architect): Ravi Chandu Ummadisetti is a distinguished Generative AI Architect with over a decade of experience, known for his pivotal role in advancing AI initiatives at Toyota Motor North America. His expertise in AI/ML methodologies has driven significant... Read More →
Kordel brings a diverse background of experiences in robotics and AI from both academia and industry. He has multiple patents in advanced sensor design and spent much of the past few years founding and building a successful sensor startup that enables the sense of smell for robotics... Read More →
If 2024 is the year of LLMs, then 2025 will be the year for LLM Applications. As LLMs continue to integrate into various applications ranging from chatbots and search engines to creative writing aids, the need to monitor and comprehend their behaviors intensifies.
Observability plays a crucial role in this context. It involves the systematic collection and analysis of data to enhance LLM performance, identify and correct biases, troubleshoot issues, and ensure AI systems are both reliable and trustworthy.
In this discussion, we will explore the concept of LLM observability in depth, focusing on how OpenTelemetry can fit into the world of LLM observability . Additionally, we will talk about challenges around modeling of prompts, completions, events, semantic conventions, and basically our path with the llm-sem-conv working group.
