​Join an exclusive Event with industry leaders and technology executives as we explore the architectural foundations required to deploy AI at scale. As AI moves from experimentation to production, the bottlenecks are shifting from model selection to data processing and inference optimization.

​This intimate session brings together forward-thinking leaders to exchange insights on building robust data pipelines capable of handling diverse data types, and designing inference architectures that balance latency, throughput, and cost.

​Key Discussion Topics

• ​The Multi-Modal Data Challenge: Strategies for processing, vectorizing, and orchestrating text, image, and structured data into unified AI data pipelines.

• ​Post-Training Optimization: Techniques for model alignment, fine-tuning, and preparing models for highly efficient production environments.

• ​High-Performance Serving: Navigating the economics and architectures of inference—balancing compute costs with real-time application demands.

• ​Ressource efficiency at scale: Leveraging Ray and fractional GPUs on EKS to utilize available resources while improving scalability. 

​Who Should Attend Technical Leaders, CTOs, Lead Solution Architects, Platform Owners, Product Owners

​Agenda

​12:00 PM – 1:00 PM | Arrivals & Welcome Networking Lunch -  Arrive, check-in, and enjoy light refreshments while connecting with fellow technology executives and subject matter experts.

​1:00 PM – 1:30 PM | Welcome & Opening Remarks A brief introduction framing the evening's discussion, highlighting the current enterprise AI landscape and the critical transition from AI proof-of-concepts to robust, scalable production. - Robert Nishihara - CoFounder Anyscale(Ray) 

​1:30 PM – 2:15 PM | Session 1: The Foundation – Multi-Modal Data Processing for AI Pipelines To build truly context-aware AI applications, enterprises must break down data silos. This session covers the strategic approach to building AI data pipelines that can seamlessly ingest, process, and synchronize multi-modal data (text, image, audio, and structured enterprise data).

​2:15 PM – 2:45 PM | Voice of a Customer -  BMW Connected AI - Fine Tuning and Serving LLMs for Connected Vehicles with Ray

​2:45 PM – 3:30 PM | Session 2: The Execution – Navigating Post-Training and Inference Serving The true cost of an AI application is realized during inference. This session focuses on the "last mile" of AI deployment. We will discuss strategic approaches to post-training (fine-tuning, alignment, and optimization) and how to architect serving infrastructure to maximize ROI.

​3:30 PM – 4:15 PM | Session 3: Increase resource efficiency at Scale: Leveraging run.ai with KubeRay on EKS to leverage available resources while improving scalability. We will discuss typical scenarios to achieve maximum value and dive into the architecture. 

​4:15 PM – 4:30 PM  Conclusion 

​4:30 PM | Executive Dinner & Networking Continue the conversation over a curated dinner. Forge valuable connections with peers who are solving the same complex data and AI scaling challenges in their respective organizations.

​7:00 PM | Event Concludes