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Data Sharing, AI/Machine Learning, Governance

In Person

Breakout

Energy and Utilities, Manufacturing

Intermediate

West, Level 2, Rm 2004

John O'Brien

Product Manager

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John is the Product Manager for Data, Analytics, and AI at Shell Energy Australia Trading & Supply. With 25+ years of experience, he specializes in transforming innovative data solutions into production realities. In 2022, he implemented the first Lakehouse at Shell Energy Trading, now ingesting 1300+ datasets from 20+ sources using both structured streaming and batch techniques. Business users create data products managed directly in Unity Catalog. John leverages Databricks and AI techniques to revolutionize data governance in production. He’s a passionate speaker, sharing his experiences and tips with the community.

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https://microsites.databricks.com/data-ai-summit-2024/speaker/john-obrien

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Data Governance

AI and the Lakehouse: Shell’s Journey Towards Effective Data Governance | Databricks

Join our speakers from Shell Energy as they discuss the challenges and solutions in implementing a data strategy and lakehouse in a large, diverse organization. They’ll share their experiences, including initial hurdles in data strategy and governance, and how they used Unity Catalog and a business-owned data product approach to overcome them. They’ll delve into the concept of Data Mesh, discussing the roles of the product team and the customer, and provide real-life examples. They’ll also share insights on using analytics, PowerBI, ML models, and AI for Data Governance. This session is for professionals grappling with data strategy and governance, offering insights on aligning goals, identifying use cases, team organization and scaling operations. Attendees will gain a deeper understanding of achieving success with data, analytics, and AI. The session, aimed at an intermediate level, will include code snippets and working code demonstrations.

https://microsites.databricks.com/data-ai-summit-2024/session/ai-and-lakehouse-shells-journey-towards-effective-data-governance

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AI and the Lakehouse: Shell’s Journey Towards Effective Data Governance

Data Sharing, Delta Lake, Governance

Manufacturing

South, Esplanade, Rm 154

Quinton Stephens

Data Engineer

A recent blue collar turned white collar professional, Quinton has a passion for building solutions to solve challenges that he and others face. His journey into the tech sector began almost four years ago, when he got hooked on learning cloud architecture in his spare time. He now brings his passions for solutions architecture, data & AI and applies them to solve challenges that the Honeywell Intelligrated Lifecycle Support Services Team faces. When taking a break from the screen, Quinton can be found playing or writing music.

https://microsites.databricks.com/data-ai-summit-2024/speaker/quinton-stephens

Mitul Desai

Sr Analytic & Product Innovation Manager

After graduation with a Bachelor's degree in Electrical Engineering from the University of Cincinnati, Mitul spent roughly 5 years as a PLC software engineer. After this, he moved to program management as a Program Manager: exceeding conveyor industry customer needs, growing relationships and accounts. Roughly six years later, Mitul then landed in his current role, where he manages technical teams and the delivery of data/analytics products to move conveyer industry customer services toward new capabilities.

https://microsites.databricks.com/data-ai-summit-2024/speaker/mitul-desai

Data Engineering and Streaming

Honeywell Intelligrated’s IoT Streaming Lakehouse | Databricks

Honeywell Intelligrated automates warehouse distribution and fulfillment operations to meet demanding service level agreements, manage exponential SKU growth, satisfy rising customer expectations, and address labor challenges. We want to share our journey launching two new IoT streaming services that power self-service data collection, custom analytics, and AI-driven solutions. The IoT services stream live control system and robotics edge data from over 50 warehouse sites across North & Central America. Databricks, Unity Catalog, and Delta Live Table pipelines serve as the backbone for effective data governance and efficient streaming data processing. Our Databricks Lakehouse doesn't stop at processing semi-structured IoT data. Migrating our traditional BI workloads to the lakehouse allowed us to standardize our ingestion methods and open up new capabilities like query federation and Delta Sharing all while laying the foundation for AI customized on our enterprise and IoT data.

https://microsites.databricks.com/data-ai-summit-2024/session/honeywell-intelligrateds-iot-streaming-lakehouse

Honeywell Intelligrated’s IoT Streaming Lakehouse

GenAI/LLMs

Financial Services

Beginner

South, Esplanade, Rm 152

Sharon Zhou

CEO & Cofounder

Sharon Zhou's mission is to make AI usable and accessible by everyone. Previously, Sharon was a faculty member at Stanford University in computer science, where she created one of Coursera's largest classes, teaching 100,000+ students. She received her PhD at Stanford in generative AI, under Dr. Andrew Ng, and is featured in MIT Technology Review's 35 Under 35. Prior to her doctorate, she was a product manager at Google, and studied Classics and Computer Science at Harvard University.

https://microsites.databricks.com/data-ai-summit-2024/speaker/sharon-zhou

Data Science and Machine Learning

Software 2.0: Shipping LLMs with New Knowledge | Databricks

The next generation of software, known as software 2.0, lies in building and shipping differentiated LLMs instead of traditional software. Teaching an LLM new knowledge is a highly effective way to build a differentiated LLM—such as Github Copilot. This process goes beyond just prompting or retrieving—it also involves training the LLM, which includes instruction-finetuning, content-finetuning, pretraining, and more that you’ll learn about in this talk. But training is harder to get right. Why? It's hard to get the right data and it calls for (often tribal) domain knowledge. In this session, you'll learn about Lamini, an all-in-one LLM stack that makes LLMs less picky about the data it can learn from and makes it easy for LLMs to take in billions of new documents. Lamini exposes LLMs in easily composable functions, so every software engineer can rapidly ship differentiated LLMs and write more software 2.0.

https://microsites.databricks.com/data-ai-summit-2024/session/software-20-shipping-llms-new-knowledge

Software 2.0: Shipping LLMs with New Knowledge

Deep Dive

Enterprise Technology

South, Level 2, Rm 205

Jerry Liu

Co-founder and CEO

Jerry is the co-founder/CEO of LlamaIndex, a data framework for building LLM applications. Before this, he led ML monitoring at Robust Intelligence, did self-driving AI research at Uber ATG, and worked on recommendation systems at Quora.

https://microsites.databricks.com/data-ai-summit-2024/speaker/jerry-liu

Generative AI

Building Production RAG Over Complex Documents | Databricks

Large Language Models (LLMs) are revolutionizing how users search for, interact with, and generate new content. Some recent stacks and toolkits around Retrieval-Augmented Generation (RAG) have emerged, enabling users to build applications such as chatbots using LLMs on their private data. However, while setting up naive RAG is straightforward, building production RAG is very challenging, especially as users scale to larger and more complex data sources. A classic example is a large number of PDFs with embedded tables. RAG is only as good as your data, and developers must carefully consider how to parse, ingest, and retrieve their data to successfully build RAG over complex documents. This session provides an in-depth exploration of this entire process; you will get an overview of the process around building a RAG pipeline that can handle messy, complicated PDF documents. This includes implementing a parsing strategy for parsing a complex document with embedded objects. This consists of an indexing strategy to process these documents beyond simple chunking techniques. We will then explore various advanced retrieval algorithms to handle questions about the tabular and unstructured data and discuss their use cases and tradeoffs.

https://microsites.databricks.com/data-ai-summit-2024/session/building-production-rag-over-complex-documents

DATA INTELLIGENCE FOR ALL

2024 FEATURED SPEAKERS

SESSIONS

AI and the Lakehouse: Shell’s Journey Towards Effective Data Governance

Honeywell Intelligrated’s IoT Streaming Lakehouse

Software 2.0: Shipping LLMs with New Knowledge

Building Production RAG Over Complex Documents

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