In the fast-paced world of data driven decision making, organizations are grappling with the challenge of realizing insights as quickly and efficiently as possible. While data products have been quite a steady architectural pillar within data ecosystems, AI has recently taken the world by storm - helping to accelerate insights at a pace previously unimaginable. In this session hear from Fahad Ahmad, data science leader at Halliburton, about their strategy to transform Halliburton’s previous data swamp into a decentralized data mesh architecture utilizing open AI and data products to deliver real-time insights. Fahad will discuss eliminating fragile data pipelines, fast data-driven decision making on curated datasets, and the innovative usage of ChatGPT to expedite the creation of data products.

The emergence of pre-trained deep learning models – e.g., Foundation and generative AI models – such as GPT-4, represents a paradigm shift in the utilization of AI. This keynote, rooted in the insights from "AI iQ™ for a Human-Focused Future," explores the profound differences between implementing these state-of-the-art pre-trained models and traditional machine learning/AI approaches. Find out why the ‘xOps’ paradigm is a fallacy, why the unique technical and operational challenges necessitate sophisticated data and model observability and the imperative for advanced and specialized computational infrastructure to support the intensive demands of these models.

We delve into the strategic considerations surrounding data governance, ethical AI use, and the integration of AI into business processes, underscoring the need for a comprehensive framework that encompasses technological implementation and alignment with organizational goals and values. The discussion extends to the critical role of having a single leader for AI – the Chief AI Officer.

Through a blend of strategic insights and practical examples, this presentation offers a roadmap for businesses looking to navigate the complexities of integrating these models. It provides actionable strategies for building the infrastructure, culture, and processes necessary to harness the transformative potential of these technologies, driving innovation, efficiency, and growth in the digital era. Join us to explore how your organization can transition from traditional AI methodologies to leveraging the power of generative models and observability ensuring a competitive edge in the rapidly evolving corporate landscape. 

Following the explosion of ChatGPT onto the market in late 2022, LexisNexis Legal & Professional quickly saw the massive opportunity for large language models (LLMs) to transform how its customers do legal work. Within weeks, the company pivoted to begin innovating, delivering, and now expanding generative AI solutions for its customers. Across legal and tech industries, the company was recognized as a generative AI leader.

LexisNexis EVP & CTO Jeff Reihl will detail how the company rapidly innovated, delivered, and expanded generative AI solutions. Reihl will share insights into how the company considers its customers’ key challenges and its multi-model approach, prioritizing the best LLM to solve each customer use case. He’ll also share how the company integrates its data assets with LLM technology to improve model output and how the company’s Retrieval Augmented Generation (RAG) platform creates flexibility for the company to adopt new LLM technologies as they change. 

Attendees will learn strategies for deploying generative AI in their organizations to impact products, customers, and internal tools. Reihl will share how attendees should think about deploying generative AI in their organization, how to hire for it, and how to organize teams around it.

For at least a decade, we have established best practices for handling data and making sure it is delivered when needed and is correct. But with the explosion of LLMs and Transformer models, not all of those best practices apply anymore. In this talk, you will learn which best practices still make sense in this new Machine Learning world, and what new strategies you need to adopt to make sure that your LLMs can train properly and return inference in a reliable, timely and *accurate* manner.

Large Language Models like the GPT, Gemini, Gemma and Llama series are rapidly transforming the world in general and the field of data science in particular. This talk introduces deep-learning transformer architectures including LLMs. Critically, it also demonstrates the breadth of capabilities state-of-the-art LLMs can deliver, including for dramatically revolutionizing the development of machine learning models and commercially successful AI products. This talk provides an overview of the full lifecycle of LLM development, from training to production deployment, with an emphasis on leveraging the open-source Python libraries like Hugging Face Transformers and PyTorch Lightning.

As the field of data analytics continues to progress and expand, the role of semantic layers in harnessing the power of AI is becoming increasingly crucial. The incorporation of context and constraint is essential to optimizing the potential of Language Model Models (LLMs), which requires a more structured and specialized approach. While traditional methods have made strides in providing some context through prompt engineering and knowledge graphs, semantic layers offer unparalleled clarity and efficiency in bridging the gap for LLMs. To further unfold the narrative on semantic layers and their transformative impact on AI-enabled analytics, we invite you to a thought-provoking session with Artyom Keydunov, Cube's CEO & Co-founder.

In this session, analytics expert and renowned AI author David Boyle will take the audience on a fascinating journey into applied AI. We'll explore how language models like ChatGPT are revolutionizing analytics, sharing dozens of practical examples of how AI is transforming data interpretation, decision-making, and communication.

We'll explore using these tools in planning analytics to generate hypotheses and analytical strategies. David will demonstrate GPT-4's Code Interpreter executing complex data tasks like cleaning and analysis, allowing attendees to see firsthand see the iterative nature of working with AI: How it can write code, perform analysis, and refine outputs based on feedback.

Finally, we'll look at bringing insights to life. AI can turn complex findings into compelling stories for decision-makers. It can generate visualizations, interpret results, and translate technical terms into actionable business insights. This helps close the gap between data teams and leadership by serving as a mediator.

This session will be far more than a simple demonstration of AI's capabilities, but instead offer a roadmap for harnessing AI's power to revolutionize your work. Join David to consider the broader implications of AI in decision-making and how to integrate these tools into your workflows.

AI has been with us for years in everyday technologies such as mapping, facial recognition, and cancer detection. But ChatGPT and its cousins made AI "real" to the masses by letting us interact with the AI directly, rather than having it work its magic behind the scenes of an app. Where might this strong AI tailwind take us? Will it push us to AI sentience or will we, as AI’s engineers, be hoisted with our own petard—or will it be a little of both? In this keynote, startup and AI pioneer Jana Eggers will share her observations and advice from delivering large-scale AI-enabled systems from more than 30 years across research, supply chain optimization, search engines, travel, and generalized decision support.