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.

Hear about the latest trends in LLMs & how this cutting-edge technology can transform AI projects & make them more efficient & effective.

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.

The introduction of Generative AI in the enterprise heralds a new era of advanced analytics and operational efficiency.

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.

90% of people in Gen AI today weren't doing it 2 years ago. This talk covers what data engineers need to launch AI projects.

In the rapidly evolving world of enterprise AI, traditional monolithic approaches are giving way to more agile and efficient architectures. This session will delve into how Multi-Agent Retrieval-Augmented Generation Systems (MARS) are transforming enterprise software development for AI applications. Learn about the core components of AI agents, the challenges of integrating LLMs with enterprise data, and how to build scalable, accurate, and high-performing AI applications

Combining cutting edge AI and anonymised hiring, ai.r Recruit is on a mission to level the playing field and help companies hire fairer and faster.

Why Attend? This session will equip you with the foresight and practical knowledge to integrate GenAI into your strategy successfully. You'll gain knowledge from real-world examples, helping you to embrace the next phase of AI development confidently.

• Look ahead to the future of Generative AI as we discuss emerging trends and new possibilities including autonomous agents and interactive AI. 

• We'll discuss how GenAI will continue to shape our world and what to expect in the coming years. 

• Get practical advice on how businesses can integrate GenAI into their operations, train their teams, and navigate data privacy and responsible AI.

• We'll share real-world success stories from industries like healthcare, finance, and entertainment, illustrating how GenAI is revolutionising these fields and making a significant impact on our daily lives.