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How to Become a True Dataviz Pro by Nick Desbarats

Globally recognized educator and best-selling author, Practical Reporting Inc.

Nearly every company struggles to create engaging charts that convey data and information effectively to stakeholders. Most charts fail because of basic issues: poor chart type choices, poor scale formatting choices, poor color choices, and a host of other basic design problems. In this eye-opening talk, globally recognized data visualization educator and best-selling author Nick Desbarats explains exactly what it takes to learn the basic “spelling and vocabulary of data visualization,” and how to become a true data visualization pro, able to design clear, compelling charts every time.

Data Automation with LLM by Rami Krispin

Senior Manager - Data Science and Engineering, Apple

Obtaining the required data for effective decision-making can be challenging due to data engineering or data science resource constraints, leading to delays, inefficiency, and potential losses. This talk will focus on creating a self-serve bot (e.g., Slack bot) that can serve data requests and support ad-hoc requests by leveraging LLM applications. This involves building a natural language to SQL engine using tools such as OpenAI API or open-source models that leverage the Hugging Face API.

From Research to the Enterprise: LLM for Enhanced ETL, Analytics, and Deployment by Ines Chami

Co-founder and Chief Scientist, NUMBERS STATION AI

As Foundation Models (FMs) continue to grow in size and capability, data is often left behind in the rush toward solving problems involving documents, images, and videos. This talk will describe our research at Stanford University and Numbers Station AI on applying FMs to structured data and their applications in the modern data stack. We’ll start with ETL/ELT, then move up the stack and discuss our work at Numbers Station to use FMs to accelerate data analytics workflows. We will then conclude this talk by discussing challenges and solutions for production deployment in the modern data stack.

AI Resilience: Upskilling in an AI Dominant Environment by Leondra Gonzalez

Senior Data & Applied Scientist, Microsoft

The boom of generative AI and LLMs have taken the world by storm. This development has already disrupted various industries and roles, and data science is no exception to that rule. In a world of embeddings and transfer learning, one might beg to question "What should I learn next?" and "Where should I spend my time and energy for deep dives?". This talk aims to guide existing AI practitioners on how to maintain relevant skills in an increasingly automated world, and how to stand out in an oversaturated job market.

Beyond Theory: Effective Strategies for Bringing Generative AI into Production by Heiko Hotz

Generative AI Global Blackbelt, Google

In the rapidly evolving and constantly advancing landscape of AI, foundation models and the broader world of generative AI have emerged as potential game-changers, offering unprecedented and previously unimagined capabilities across a wide variety of domains and use cases. However, the practical challenge of transitioning from conceptual research to full-scale production-level applications remains a major obstacle that many organizations and teams continue to face. This session takes a deep dive into exploring pragmatic and actionable strategies and best practices for successfully integrating these cutting-edge AI technologies into real-world business environments, and will equip you with directly actionable knowledge and tools to successfully meet the challenges in navigating the world of FMOps/LLMOps.

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