This session looks at the ever-increasing demand for data and AI, the current challenges slowing development and how companies can overcome these challenges and shorten time to value using generative AI and open tables like Apache Iceberg. It also looks at how this approach makes it possible to transitioning away from siloed analytical systems to a modern data architecture where multiple teams can create reusable data products across multiple clouds and op-premises environments using generative AI in Data Fabric and share that data across multiple analytical workloads. 

This session explores Gemini's capabilities, architecture, and performance benchmarks. We'll delve into the significance of its extensive context window and address the critical aspects of safety, security, and responsible AI use. Hallucination, a common concern in LLM applications, remains a focal point of ongoing development. This talk will highlight recent advancements aimed at mitigating the risk of hallucination to enhance LLMs utility across various applications.

Snowflake had a big challenge: How do you enable a team of 1,000 sales engineers and field CTOs to successfully deploy over 100 new data products per week and demonstrate every feature and capability in the Snowflake AI Data Cloud tailored to different customer needs?

In this session, Andrew Helgeson, Manager of Technology Platform Alliances at Snowflake, and Guy Adams, CTO at DataOps.live, will explain how Snowflake builds and deploys hundreds of data products using DataOps.live. Join us for a deep dive into Snowflake's innovative approach to automating complex data product deployment — and to learn how Snowflake Solutions Central revolutionizes solution discovery and deployment to drive customer success.

In today’s rapidly evolving technological landscape, the integration of data within organisations is not just a trend but a necessity. This panel discussion will explore how data literacy and the adoption of a data-driven culture can act as catalysts for significant organisational change. We will delve into the roles of Chief Data Officers, Chief Innovation Officers, and Chief AI Officers, examining whether history is repeating itself with new and emerging roles. The discussion will be punctuated by shifts in technology capability and will address whether AI is a true catalyst for organisational change.

In a world where Artificial Intelligence is the new normal, interpersonal skills like critical thinking, persuasion and emotional intelligence will sit alongside the traditional skillset of the data leader as businesses are now scaling and monetising their AI initiatives. 

Organisations must ensure that their leadership is balanced to avoid bias and ensure relevance to the customer, and the leader of the future will be the linchpin to ensure that the opportunity from AI is realised. o how should businesses nurture emerging leaders to ensure that they are developing and retaining top talent in an age of acute skills shortage and salary inflation? 

And how can future leaders equip themselves with the right skills and networks to build sustainable careers right up to the C-suite? 

Join this panel of experts as they discuss the future of leadership in a world where artificial intelligence is central to decision making and why getting it right is a business imperative.

The Dell AI Factory with NVIDIA is a framework to accelerate and de-risk AI adoption and AI powered innovation in the enterprise. Join us to explore how – with this open and extensible end to end solution – we help organisations align the right use case to the most impactful business outcomes.

We will showcase how organisations are leveraging our broad range of capabilities and ecosystem of partnerships, to take advantage of their enterprise data. From the edge, through to the multicloud and private data centre environments, together we’ll explore how to build differentiated and effective business capabilities. 

Everything has changed in the last year with Generative AI entering onto the scene. This means a re-shuffling of priorities and budgets, putting AI-enabled Data & Analytics right back at the top of the agenda. In this session we will discuss: 

• That there is no Generative AI without data – but it has to be the right data 

• The importance of being able to bring together organised and trusted data 

• Why your data integration strategy is the foundation to successfully using AI

2024 is the year of the AI agent. But what are AI agents and how are they different from traditional chatbots we all know? In this talk, we’ll dive into how AI agents work and what makes them different from legacy chatbots. Listeners will leave with a good understanding of AI agent architecture and their newly unlocked capabilities.

In an increasingly multicultural and globalised world, understanding cultural identity has never been more vital. With more than 200,000 years of human evolution, our capacity for culture has thrived as we journeyed from the equator to every part of the globe. We’ve learned, traded and sometimes battled with a growing and diverse global population. Today, our diversity is more significant than ever, especially as we grapple with the lightning-fast evolution of AI technology and look for solutions to global challenges.

Join us in this session as we draw from historical insights that remain highly relevant for our collaboration in a modern context. We’ll explore the cost of conflicts and the disproportionate investment required to better understand our diversity. What does a nuanced, multifactorial, data-driven approach to cultural diversity entail? And what is the price we pay when we oversimplify methods of measuring and categorising our most precious asset – our multifaceted culture.

Simplify your GenAI journey and unlock the hidden power within your databases. Businesses often feel pressured to adopt new, specialized technologies to stay ahead. However, the power to revolutionize your applications with GenAI may already reside within your current database infrastructure. 

We’ll build understanding of vector capabilities, ease of use/ROI, and how PostgreSQL, enhanced with the pgvector extension, can address 80% of common GenAI use cases, providing a streamlined and cost-effective path to AI-driven solutions.

Join us to demystify the hype around dedicated vector databases and explore how built-in vector capabilities existing databases can efficiently support your GenAI workloads without extra overhead.

Recent advances in artificial intelligence have sparked both wonder and anxiety as we contemplate its transformative potential. To nurture a future where AI is leveraged to the benefit of people and society it is vital to understand the importance of responsible AI practices, guided by principles such as fairness, inclusiveness, and transparency.

In this session we will discuss practical tools and resources for implementing these practices, as well as the role of the Responsible AI and Effects in Engineering and Research.

Elsevier is a leading provider of quality scientific data to the global research sector. We are all too aware that high-quality, well-structured data is the cornerstone of any data-driven product – particularly relevant as we are caught in the disruptive excitement of the Gen AI wave. We mustn’t lose sight of the role good data plays – garbage in garbage out is as applicable now as ever.

The generation and availability of high-quality data relies on good data governance and the adoption of FAIR (Findable, Accessible, Interoperable, Reusable) data principles, including ontologies. Our semantic technology stack and domain expertise helps drive this adoption. Structured data, such as ontology-tagged text and Knowledge Graphs can be the bedrock of explainable GenAI solutions such as we are seeing in the arena of scientific search.

Join Reema Vadoliya as she explores the transformative potential of inclusive data practices in shaping a more equitable future. Reema delves into the challenges faced across the data industry and society, drawing from her personal experiences and insights. Through practical examples and case studies, she demonstrates how challenging bias in AI begins with fostering inclusivity and representation in data collection. By envisioning a future where data is crafted with inclusivity in mind, Reema inspires participants to embark on a journey towards building a more ethical and inclusive AI ecosystem. Key Takeaways: - Empowering Data Practices: Reema highlights the transformative potential of inclusive data practices, empowering organisations to challenge bias in AI through prioritising inclusivity and consent in data collection - Insightful Data Insights: Reema demonstrates how inclusive data practices lead to impactful insights, showing attendees how embracing diversity in data collection results in higher response rates and deeper audience understanding. - Vision for Ethical AI: Reema inspires attendees to envision a future where data is crafted with inclusivity, fostering fairness and transparency in data-driven decision-making to drive towards an ethical and equitable AI ecosystem.

Join Experian, Sainsbury’s, The Nottingham, UST and British Business Bank discuss how better data quality and better data governance leads to improved AI. Hear real business examples of how AI is being implemented and the lessons our panellists wished they’d known sooner. Also learn key takeaways on how to have a better Data Governance strategy and why having trust in your data is more important than any new emerging technology.

As organizations transition from digital to AI-native, data becomes the linchpin of innovation, empowering AI to turn raw information into actionable insights. Cloudera hybrid data platform brings all data to modern use cases including Generative AI. This session explores how Cloudera can help your organization deploy robust AI use cases to production faster, without compromising performance, accuracy, and security.

Learn about IBM InstructLab, which streamlines the fine-tuning of AI models through knowledge distillation. Discover how this cutting-edge technology can transform your AI projects and make them more efficient and effective.

In addition, we’ll delve into the latest trends in Large Language Models (LLMs), highlighting the benefits of enterprise-ready models such as IBM Granite. We’ll discuss key considerations such as model size, purpose, and the debate between open-sourced and closed models.

In the world of GenAI, advancements are happening at a crazy speed. These advancements concern not only the algorithms but also the operations side of things. In this talk, we will go back to the basics, discuss the main principles of building robust ML systems (traceability, reproducibility, and monitoring), and explain what types of tools are required to support these principles for different types of applications.

Buckle up and join Tomas Trnka, Chief Data Officer at Carvago, as he unveils the journey behind Carvago’s rapid rise as a leading online marketplace for used cars. Discover how Carvago built a production-ready solution in just 3 months using the Keboola platform. Tomas will dive into the architecture “under the hood”, showing how they process over 5 million car classifieds daily and iteratively develop new data products. Part of the presentation will be dedicated to Data Mesh, with real-world examples of its implementation. Finally, learn about Carvago’s unique approach to AI, blending classic machine learning with AI enhancements. Attendees will gain practical insights and recommendations for accelerating data initiatives and building scalable, cutting-edge solutions.

How the development of GenAI affects representation and diversity Based on the work around moral usage of AI and wider themes of diversity and inclusion in data and SaaS companies, I'll be looking at current trends in the space and how to help establish better practice around representation.

Step into the dynamic world of data governance, business operations, and artificial intelligence (AI), where the unsung hero, metadata, takes center stage. Just like the perfect sandwich relies on clear definitions of its ingredients, this talk unveils the indispensable role of metadata in defining and organizing data. George will share captivating real-life stories and examples on how clarity in definitions and metadata not only streamlines operations but also empowers decision-makers with invaluable insights. Explore the backbone of AI advancement through essential data management tools: the Business Glossary, Data Dictionary, Data Catalog, and Machine Learning Metadata Store. Let's embark on a journey where unified interpretations pave the way for accuracy, efficiency, and success in the data-driven era.

In his keynote talks at the Snowflake and Databricks Summit this year, Jenson Huang, the Founder CEO at NVIDEA, talked at length about how, to compete today, organizations have to build data flywheels: where they take their proprietary business data, use AI on that data to build proprietary intelligence, use that insight to build proprietary products and services that your customers love and use that to create more proprietary data to feed AIs to build more proprietary intelligence and so on.

But what does this mean in practice? Jenson's example of NVIDEA is intriguing - but how can the rest of us build data flywheels in our own organizations? What practical steps can they take?

In this talk, Yali Sassoon, Snowplow cofounder and CPO, will start to answer these questions, drawing on examples from Snowplow customers in retail, media and technology that have successfully built customer data flywheels on top of their proprietary 1st party customer data.

The introduction of Generative AI in the enterprise heralds a new era of advanced analytics and operational efficiency. By harnessing the sophisticated capabilities of Gen AI, businesses can significantly accelerate their decision-making processes and empower their employees across multiple dimensions. Gen AI enables intricate data analysis, natural language processing, and decision-making with just a few prompts, facilitating faster innovation and competitive advantage.

However, implementation and optimization of Gen AI for enterprise analytics use cases present several challenges. Gen AI is hard to put into production, due to the complexities associated with data integration and secure data access. Additionally, enterprises struggle to tune and deliver consistently high quality and compelling responses to AI-driven questions.

Join this session to learn how implementing a data fabric can help accelerate time to value and enable Generative AI.

Crafting Tech Stacks to Embrace Traditional and Generative AI in Enterprise Environments In this talk, Bas will present a reference architecture for machine learning systems that incorporates MLOps standards and best practices. This blueprint promises scalability and effectiveness for ML platforms, integrating modern technological concepts such as feature stores, vector stores, and model registries seamlessly into the architecture. With a spotlight on emerging generative AI techniques like retrieval-augmented generation, attendees will gain valuable insights into harnessing the power of modern AI practices. Additionally, Bas will delve into the aspects of MLOps, including feedback loops and model monitoring, ensuring a holistic understanding of how to operationalize and optimize ML systems for sustained success.

We have a hypothesis, that 90% of people doing Gen AI today weren’t doing it two years ago. The landscape is full of people stumbling their way through it, from the AI academics learning that code for papers is not software development ready, all the way to data experts suddenly needing to learn a new skill.

In this talk, we'll go through what data engineers need to know to help get those AI projects off the ground. Starting with picking the right projects, execution plans, through to toolsets and skills that will make you shine.