ARCHITECTING CUSTOM AI MODELS AND SCALABLE INFRASTRUCTURE FOR ENTERPRISE ADOPTION AND VALUE GENERATION
January 13th-15th, 2025 |
Hilton London Syon Park
Main Day 2 - Wednesday 15th January 2025
8:35 am - 8:40 am
Chair Remarks
8:40 am - 9:20 am
PANEL: Delivering Enterprise Specific AI Workloads with Cost Effective and Efficient Infrastructure
Richard Kiernan -
Global Head of AI & Machine Learning Platforms,
Natwest RBS Group
- Ensuring AI-Ready Data: Learn how to manage and prepare high-quality data to power AI models effectively, ensuring optimal results in dynamic and data-rich environments.
- Successful AI deployments, focusing on the infrastructure strategies that enabled businesses to boost operational efficiency, enhance productivity, and drive revenue growth while reducing costs.
- Optimising AI Infrastructure for Business Demands: Explore strategies to manage increasing computational requirements and overcome challenges like latency, power consumption, and compute access, with a focus on cost-efficient, scalable solutions that align with business objectives.
- Enhancing AI Deployment with MLOps: Streamlining AI model lifecycle management, optimizing infrastructure, reducing costs, and improving scalability and reliability in enterprise settings.
9:20 am - 9:50 am
SPONSORED SESSION: Driving Business Transformation with Scalable AI Solutions
- Scalable Enterprise AI: Learn how to scale AI across departments for enterprise-wide impact and transformation.
- Cost-Effective AI Infrastructure: Uncover solutions for managing high computational demands while ensuring flexible, scalable, and cost-efficient AI deployments.
- AI-Powered Business Models: Discover how to seamlessly integrate AI into business operations, unlocking innovation and growth.
9:50 am - 10:30 am
PANEL: Organisational Workflows and Collaboration in Delivering AI-Powered Products
Rowena Rix -
Head of Innovation and AI,
Dentons
Trevor O'Brien - Senior Director, Technology Innovation, Moody's Analytics
- The need to foster enhanced collaboration between Product, Data Science, Engineering, and User Experience teams to develop and launch AI applications.
- Empowering cross-functional teams to innovate and scale AI initiatives. How this enables tighter integration of AI capabilities into end-to-end business processes, ensuring that insights generated by AI are rapidly translated into user-centric product enhancements and efficient engineering solutions.
- Emphasising the importance of responsible AI practices, ensuring that AI systems are designed and deployed in a manner that upholds ethical standards, transparency, and fairness, which are critical for sustainable market success.
- How a robust AI infrastructure can facilitate the seamless integration of AI into organizational workflows, driving enterprise-wide transformation and accelerating the delivery of innovative AI-powered products to market.
10:30 am - 11:00 am
Networking & Refreshments
11:00 am - 11:05 am
Chair Remarks
11:05 am - 11:30 am
Optimising Small Language Models (<= 7B parameters) on commodity hardware to address different requirements (costs, privacy, safety, regulations, etc.) in the BioTech Manufacturing
11:30 am - 11:55 am
SPONSORED SESSION: Enhancing AI Efficiency: Navigating Hardware and Software Solutions for Optimized AI Workloads
- Addressing Scalability and Performance Challenges: Explore strategies to balance cost, power consumption, and performance when deploying AI on constrained hardware.
12:00 pm - 12:25 pm
Creating Value and Solving Real-World Problems by Applying AI and Edge Computing
Nicolas Maillot -
Chief Architect - Data, AI, Edge Computing,
ThalesAlenia Space
12:25 pm - 1:05 pm
PANEL: Transforming AI Workloads with Scalable Hardware and Software Integration
- Overcoming AI Architecture Challenges: Address scaling issues in distributed systems and the architectural shifts needed for AI-driven demands.
- Leveraging Hardware Acceleration: Explore how FPGAs, ASICs, and specialized processors enhance AI performance and energy efficiency.
- Maximising Hardware Utilization: Learn techniques for optimizing software to fully exploit advanced GPUs and processors for efficient AI training and inference.
- Aligning Software and Hardware: Discover how synchronizing development cycles can overcome bottlenecks, improving scalability and efficiency in AI deployments.
1:05 pm - 2:00 pm
Lunch
2:00 pm - 2:05 pm
Chair Remarks
2:05 pm - 2:30 pm
Strategic and Responsible Development of AI Products: Balancing Innovation with Architecture, Design & Ethical Considerations
2:30 pm - 2:55 pm
Scaling Impact: Save the Children's AI Product Development Strategy
Nicholas Drabowski -
Head of Generative AI Workstream,
Save the Children
- Cost-Effective Resource Management & Donor Alignment: Leverage AI infrastructure to maximize resource efficiency, aligning with third-party donor expectations while minimizing costs.
- Eliminating language barriers in RAG knowledge search for Global Operations: Develop AI systems that handle diverse languages effectively, ensuring that solutions are accessible and functional across all regions.
- Achieving Scalable Product Solutions: Design AI-driven products that can be scaled globally, focusing on maintaining cost-efficiency, reliability, and adaptability to diverse environments.
2:55 pm - 3:20 pm
From Research to Reality: Innovating and Creating User Centric AI products from Lean Data
Martina Johannesson -
Senior Technical Product Manager,
Unity Technologies
3:20 pm - 3:45 pm
How to Integrate AI Products and Lead Transformation of a Business Operating Model
3:45 pm - 4:10 pm
Networking & Refreshments
4:10 pm - 4:15 pm
Chair Remarks
4:15 pm - 4:55 pm
PANEL: Enabling AI-Ready Data Platforms and Build Valuable AI Products
Antoine Castex -
Group Data Architect,
L'Oreal
- The critical role of high-quality, actionable data in powering AI and generative AI (GenAI) initiatives.
- Strategies for safeguarding data as a key enterprise asset, ensuring its integrity, privacy, and security while maximizing value from AI-driven insights.
- Ensuring data is findable, accessible, trustworthy, interoperable, and reusable to enhance AI outcomes.
- Implementing and enforcing robust data governance policies through advanced data platforms.
- Exploring the future of business models shaped by the integration of AI into products and services.
4:55 pm - 5:20 pm
Platform Architecture for Repeatable AI Value
- Scalable and Extensible AI Platforms: Examine the principles of building an AI platform that can scale with evolving business demands, offering extensibility and adaptability to support continuous innovation and long-term value creation.
- Tailored Infrastructure for AI Workloads: Explore the importance of designing platform infrastructure that is specifically aligned with unique AI workloads, ensuring that AI systems drive consistent and scalable business value.
- The need for a robust platform that supports the entire AI lifecycle—from data management and model training to deployment—while emphasizing the role of MLOps in ensuring repeatability and reliability. Highlight the importance of integrating AI across business applications and continuously iterating based on feedback to align the platform with evolving business needs.
5:20 pm - 6:00 pm
PANEL: Executing a Strategy and Delivering Value from AI Infrastructure and Architecture Investments
Jie Zheng -
Technology Team Lead - Machine Learning Lab,
TUI
- Accelerating Innovation: A strong AI infrastructure speeds up model deployment and experimentation, driving innovation and giving organizations a competitive edge. Building and retaining a skilled AI team ensures sustained innovation.
- Improving Efficiency: AI infrastructure automates processes, freeing up human resources for strategic tasks. Hiring and retaining top talent is key to fully leveraging these efficiencies.
- Enabling Scalability: Scalable AI platforms allow businesses to grow their initiatives with the organization. A team of experts ensures seamless scaling and continued performance.
- Enhancing Decision-Making: AI enables better data-driven decisions by processing large data volumes. A strong team of data experts is critical for interpreting insights and executing on strategy.
