• Tools and techniques for model selection, training, and customisation, including foundation models, fine-tuned models, and centralised repositories to streamline AI development.
• Deploying and Orchestrating AI/ML Models: How to effectively deploy, distribute, and orchestrate AI/ML models to ensure your solution is accurate, reliable, and scalable. Considering specific requirements for training and inference when designing AI/ML solutions. How to establish and meet performance benchmarks for different components of your system, ensuring optimal operation.
• The importance of a robust infrastructure layer, including cloud platforms and specialized hardware (GPUs, TPUs), to support the intensive training and inference workloads of generative AI models.
• How platform engineering for MLOps enhances the orchestration of AI models, focusing on adapting, deploying, and monitoring models effectively within end-user applications.

Alberto Romero

Director, GenAI Platform Engineering
Citi

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Mayank Srivastava

Global Director Data and Analytics
Frieslandcampina

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Andre Dourson

Next Generation Technologies Director
MARS Petcare

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Yeunjin Kim

Head of Data Science
Mott Macdonald

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Hamza Tahir

Co-Founder
ZenML

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• Modernising enterprise data and data science platforms and introducing GenAI models with LLMs.
• Creating data science models which will generate ROI

Kumaravel Vivekanandam

Director, Data Platform Engineering & Corporate Data Governance
Abercombie & Fitch

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• Managing AI Compute Resources: Overcoming hurdles in managing compute resources like GPUs and TPUs is crucial for efficient AI deployments. Limited access can delay projects and drive-up costs, but tailored infrastructure solutions can optimize resource allocation and improve overall efficiency.
• Architecting for Scalability and Performance: Developing a robust structural framework and best practices for AI infrastructure is key to ensuring scalability, high performance, and cost optimization. Standardized reference architectures help simplify resource management and meet the growing demands of AI systems.
• Optimizing Workflows and Integration: Efficiently allocate computational resources and optimize workflows by integrating critical components of the AI ecosystem. This ensures seamless interaction between hardware, software, and AI models, leading to successful and scalable deployments.
• Tailored Infrastructure for Custom AI Solutions: Custom AI deployments benefit from specialized infrastructure designed to handle specific use cases. These solutions enable fast response times, cost efficiency, and scalability, ensuring the seamless integration and performance of both open-source and custom AI models.

• Delivering Enterprise-Grade Generative AI Powered by a Purpose-Built, Full Stack Platform: Strategies for integrating AI model outputs into existing business workflows.
• Adopting a product mindset for AI and data: Transforming data into a strategic asset by ensuring it is findable, accessible, trustworthy, interoperable, and reusable—all while maintaining security and privacy protocols.
• Building and utilizing data platforms that can deliver the scale, speed, and reliability required to support AI development and deployment.
• Infrastructure adaptations needed to support widespread use of foundation models. Leveraging scalable and elastic infrastructure for efficient and affordable model training and deployment.

• AI-ready data: Ensuring quality, structure, and governance to build reliable AI models.
• Best practices: Industry best practices for designing and refining custom models.
• Competitive advantage: How tailored AI models drive impact; providing a unique edge and measurable ROI in enterprise settings.
• Use case identification: Define and prioritize high-impact, business-relevant AI use cases that align with strategic goals.

Dara Sosulski

Head of Artificial Intelligence and Model Management
HSBC

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Ali Faisal

Lead AI product development
Coca-Cola Europacific Partners

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Jürgen Weichenberger

VP of AI Strategy & Innovation
Schneider Electric

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The objective of the talk is to outline that the ongoing business value is derived from operationalizing AI by consistently synchronising along four concurrent dimensions: Infrastructure, Data, Code and Model.

Each of these dimensions have traditionally different stakeholders with different imperatives, which create friction whilst bringing models from labs to live production.

ML/AI mandates to look at these 4 dimensions concurrently, consistently and coherently along the exploration, development, testing, validation and production lifecycle.

Amit Nandi

VP Solutions & Data Architect
Barclays Corporate & Investment Bank

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• 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.
• Integrating Cutting-Edge AI Models: How to seamlessly incorporate the latest AI advancements into existing infrastructure, ensuring smooth integration and optimised performance.
• Scalability Strategies for AI Models: Explore the best practices for deploying AI models that can scale across large enterprise environments, ensuring performance and efficiency.
• Scalable AI Infrastructure Solutions: Explore vendor-provided solutions that deliver specialized infrastructure for scaling AI workloads, enhancing flexibility, and maintaining cost efficiency.
• Optimising AI for Enterprise Deployment: Gain insights into real-world AI deployment strategies that tackle infrastructure, model complexity, and scalability challenges, ensuring flexibility and long-term success.
• Ensuring Security and Compliance in AI Deployments: Discover how to secure AI solutions at scale, while meeting regulatory and compliance requirements in various industries.

Neil Stobart

CTO
Cloudian

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• Adapting to the Evolution of Foundational AI Models: Strategies for staying current with the latest AI advancements and integrating them into your deployment pipeline.
• Seamlessly Integrating AI into Products: Best practices for embedding AI capabilities into your products while managing model lifecycle and user experience.
• Ensuring AI-Ready Data for Optimal Performance: The importance of preparing, managing, and maintaining high-quality data as the foundation of successful AI models.
• Scaling AI Models in a Dynamic Environment: Techniques for effectively scaling AI solutions, addressing challenges related to infrastructure, data volume, and model complexity.

Nikita Iserson

Director, Machine Learning Engineering
S&P Global

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• Scalability and delivery of AI custom workloads through specialised infrastructure, enabling rapid deployment and scaling of AI models across distributed environments.
• Insights into the capabilities necessary to deliver a custom compute platform that meets the demanding requirements of AI, ensuring optimized performance and efficiency.
• How hyperscalers are enhancing AI infrastructure, providing scalable, high-performance resources that can support vast AI workloads while maintaining flexibility and cost-effectiveness.

Shelly Henry

Director of Hardware Engineering
Microsoft

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Kamran Naqvi

Chief Network Architect – EMEA
Broadcom

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Patrick Lastennet

VP Strategic Partnerships
OPCORE

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Renato Paço

Global Director of Cloud and Infrastructure
Sky Group

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Responsible AI is the key to creating scalable and robust AI infrastructures that can sustain long-term growth while ensuring ethical practices. This presentation delves into how aligning AI operations with responsible frameworks enhances system reliability, optimizes infrastructure, and fosters trust among consumers and stakeholders. Discover strategies to mitigate risks, improve performance, and build AI systems that not only meet business needs but also uphold ethical standards for a better, more sustainable future.

Tania Dias

Global VP, AI Adoption & Governance
IKEA

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• Machine learning lifecycle and challenges in scaling ML products 

• Core components of machine learning platform and best practices enforcement

Hisham Mohamed

Director of Engineering, Machine Learning Platform
Expedia Group

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• Critical Components for Success: Process, People, and Technology: A deep dive into the three essential pillars of AI product development: optimizing processes, aligning skilled teams, and leveraging the right technologies for successful deployment.
• Enabling Scalable Enterprise AI Deployment: Strategies to implement AI solutions at scale, focusing on performance, reliability, and efficiency for large-scale enterprise environments.
• Enterprise-wide AI Integration Best Practices: Insights on integrating custom AI models across diverse platforms, focusing on scalability, consistent performance, and streamlined workflows. These practices help accelerate AI-driven innovation throughout the organization.
• Optimizing Scalable AI Infrastructure: Explore how to design infrastructure that supports seamless deployment of AI models by leveraging cloud-based platforms, distributed computing, and hybrid solutions. Learn to balance cost, performance, and operational efficiency, with a brief focus on how MLOps can play a role in ensuring scalability across the enterprise.

Donatien Chedom Fotso

Head of Applied AI – CB Data Science Center of Excellence
Deutsche Bank

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Dale Goddard

Digital Exploitation for Defence Head of Digital & Technology
UK Ministry of Defence

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Mohamed Beydia

Head of Data and Analytics
M7 Group, a Canal+ subsidiary

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Tharun Loknath

Head of AI Architecture
Just Group

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Bugra Ozer

Data Science and AI Governance Lead
Dentons

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