Ever wonder how Databricks operates its own enterprise lakehouse, where all employees and all teams inside use data and AI to solve problems and guide our decisions? Bruce Wong, head of data platforms, will talk about how his team leverages Databricks itself.

PySpark has long been a cornerstone of big data processing, excelling in data preparation, analytics and machine learning tasks within traditional data lakes. However, the rise of multimodal AI and vector search introduces challenges beyond its capabilities. Spark’s new Python data source API enables integration with emerging AI data lakes built on the multi-modal Lance format. Lance delivers unparalleled value with its zero-copy schema evolution capability and robust support for large record-size data (e.g., images, tensors, embeddings, etc), simplifying multimodal data storage. Its advanced indexing for semantic and full-text search, combined with rapid random access, enables high-performance AI data analytics to the level of SQL. By unifying PySpark's robust processing capabilities with Lance's AI-optimized storage, data engineers and scientists can efficiently manage and analyze the diverse data types required for cutting-edge AI applications within a familiar big data framework.

Deliver trusted, high-performance insights by incorporating Unity Catalog metric views and business semantics into your AI/BI workflows. This session dives into the architecture and best practices for defining reusable metrics, implementing governance and enhancing query performance in AI/BI Dashboards and Genie. Learn how to manage business semantics effectively to ensure data consistency while empowering business users with governed, self-service analytics. Ideal for teams looking to streamline analytics at scale, this session provides practical strategies for driving data accuracy and governance.

Bryan McCann, You.com’s co-founder and CTO, shares his journey from studying philosophy and meaning to the Stanford Computer Science Department working on groundbreaking AI research alongside Richard Socher. Right now, AI is reshaping everything we hold dear — our jobs, creativity, and identities. It’s also our greatest source of inspiration. The Age of AI is simultaneously a Renaissance, Enlightenment, Industrial Revolution and likely source of humanity’s greatest existential crisis. To surmount this, Bryan will discuss how he uses AI responses as new starting points rather than answers, building teams like neural networks optimized for learning and how the answer to our meaning crisis may be for humans to be more like AI. Exploring AI’s impact on politics, economics, healthcare, education and culture, Bryan asserts that we must all take part in authoring humanity’s new story — AI can inspire us to become something new, rather than merely replace what we are now.

One of the largest and trailblazing U.S. states is setting a new standard for how governments can harness data and AI to drive large-scale impact. In this session, we will explore how we are using the Databricks Data Intelligence Platform to address two of the state's most pressing challenges: public health and transportation. From vaccine tracking powered by intelligent record linkage and a service-oriented analytics architecture, to Gen AI-driven insights that reduce traffic fatalities and optimize infrastructure investments, this session reveals how scalable, secure, and real-time data solutions are transforming state operations. Join us to learn how data-driven governance is delivering better outcomes for millions—and paving the way for an AI enabled, data driven and more responsive government.

Now the largest automaker in the United States, selling more than 2.7 million vehicles in 2024, General Motors is setting a bold vision for its future, with Software-defined vehicles and AI as a driving force. With data as a crucial asset, a transformation of this scale calls for a modern approach to Data Governance. Join Sherri Adame, Enterprise Data Governance Leader at General Motors, to learn about GM’s novel governance approach, supported by technologies like Atlan and Databricks. Hear how Sherri and her team are shifting governance to the left with automation, implementing data contracts, and accelerating data product discovery across domains, creating a cultural shift that emphasizes data as a competitive advantage.

Manufacturers today need efficient, accurate, and flexible integrated planning across supply, demand, and finance. A leading industrial manufacturer is pursuing a competitive edge in Integrated Business Planning through data and AI.Their strategy: a connected, real-time data foundation with democratized access across silos. Using Databricks, we’re building business-centric data products to enable near real-time, collaborative decisions and scaled AI. Unity Catalog ensures data reliability and adoption. Increased data visibility is driving better on-time delivery, inventory optimization, and forecasting,resulting in measurable financial impact. In this session, we’ll share our journey to the north star of “driving from the windshield, not the rearview,” including key data, organization, and process challenges in enabling data democratization; architectural choices for Integrated Business Planning as a data product; and core capabilities delivered with Tiger’s Accelerator.

Join for an insightful presentation on creating a robust data architecture to drive business outcomes in the age of Generative AI. Santosh Kudva, GE Vernova Chief Data Officer and Kevin Tollison, EY AI Consulting Partner, will share their expertise on transforming data strategies to unleash the full potential of AI. Learn how GE Vernova, a dynamic enterprise born from the 2024 spin-off of GE, revamped its diverse landscape. They will provide a look into how they integrated the pre-spin-off Finance Data Platform into the GE Vernova Enterprise Data & Analytics ecosystem utilizing Databricks to enable high-performance AI-led analytics. Key insights include: Incorporating Generative AI into your overarching strategy Leveraging comprehensive analytics to enhance data quality Building a resilient data framework adaptable to continuous evolution Don't miss this opportunity to hear from industry leaders and gain valuable insights to elevate your data strategy and AI success.

Learn how to build sophisticated systems that enable natural language interactions with both your structured databases and unstructured document collections. This session explores advanced techniques for creating unified and governed AI systems that can seamlessly interpret questions, retrieve relevant information and generate accurate answers across your entire data ecosystem. Key takeaways include: Strategies for combining vector search over unstructured documents with retrieval from structured databases Techniques for optimizing unstructured data processing through effective parsing, metadata enrichment and intelligent chunking Methods for integrating different retrieval mechanisms while ensuring consistent data governance and security Practical approaches for evaluating and improving KBQA system quality through automated and human feedback

This course will introduce you to AI agents, their transformative impact on organizations, and how Databricks enables the creation of AI agent systems. We’ll begin by exploring what AI agents are, how they differ from traditional AI systems, and why they are becoming essential in today’s data-driven landscape. Next, we’ll examine how AI agents can be used to automate tasks, enhance decision-making, and unlock new efficiencies for businesses of all sizes. Finally, we’ll review real-world examples of AI agent systems on Databricks, showcasing practical applications across industries and sharing key considerations for successful adoption. You can pass a short quiz and earn a badge to validate your learning on completion.

Autonomous AI agents are transforming industries by enabling systems to perform tasks, make decisions and adapt in real time without human intervention. In this talk, I will delve into the architecture and design principles required to build these agents within scalable AI infrastructure. Key topics will include constructing modular, reusable frameworks, optimizing resource allocation and enabling interoperability between agents and data pipelines. I will discuss practical use cases in which attendees will learn how to leverage containerization and orchestration techniques to enhance the flexibility and performance of these agents while ensuring low-latency decision-making. This session will also highlight challenges like ensuring robustness, ethical considerations and strategies for real-time feedback loops. Participants will gain actionable insights into building autonomous AI agents that drive efficiency, scalability and innovation in modern AI ecosystems.

Discover how Dodo Brands, a global pizza and coffee business with over 1,200 retail locations and 40k employees, revolutionized their analytics infrastructure by creating a self-service data platform. This session explores the approach to empowering analysts, data scientists and ML engineers to independently build analytical pipelines with minimal involvement from data engineers. By leveraging Databricks as the backbone of their platform, the team developed automated tools like a "job-generator" that uses Jinja templates to streamline the creation of data jobs. This approach minimized manual coding and enabled non-data engineers to create over 1,420 data jobs — 90% of which were auto-generated by user configurations. Supporting thousands of weekly active users via tools like Apache Superset. This session provides actionable insights for organizations seeking to scale their analytics capabilities efficiently without expanding their data engineering teams.

This session is repeated. Explore how Anthropic's frontier models power AI agents in Databricks Mosaic AI Agent Framework. Learn to leverage Claude's state-of-the-art capabilities for complex agentic workflows while benefiting from Databricks unified governance, credential management and evaluation tools. We'll demonstrate how Anthropic's models integrate seamlessly to create production-ready applications that combine Claude's reasoning with Databricks data intelligence capabilities.

This in-depth session explores advanced MLOps practices for implementing production-grade machine learning workflows on Databricks. We'll examine the complete MLOps journey from foundational principles to sophisticated implementation patterns, covering essential tools including MLflow, Unity Catalog, Feature Stores and version control with Git. Dive into Databricks' latest MLOps capabilities including MLflow 3.0, which enhances the entire ML lifecycle from development to deployment with particular focus on generative AI applications. Key session takeaways include: Advanced MLflow 3.0 features for LLM management and deployment Enterprise-grade governance with Unity Catalog integration Robust promotion patterns across development, staging and production CI/CD pipeline automation for continuous deployment GenAI application evaluation and streamlined deployment

Databricks is transforming its sales experience with a GenAI agent — built and deployed entirely on Databricks — to automate tasks, streamline data retrieval, summarize content, and enable conversational AI for over 4,000 sellers. This agent leverages the AgentEval framework, AI Bricks, and Model Serving to process both structured and unstructured data within Databricks, unlocking deep sales insights. The agent seamlessly integrates across multiple data sources including Salesforce, Google Drive, and Glean securely via OAuth. This session includes a live demonstration and explores the business impact, architecture as well as agent development and evaluation strategies, providing a blueprint for deploying secure, scalable GenAI agents in large enterprises.

Protecting insurers against emerging threats is critical. This session reveals how leading companies use Databricks’ Data Intelligence Platform to transform risk management, enhance fraud detection, and ensure compliance. Learn how advanced analytics, AI, and machine learning process vast data in real time to identify risks and mitigate threats. Industry leaders will share strategies for building resilient operations that protect against financial losses and reputational harm. Key takeaways: AI-powered fraud prevention using anomaly detection and predictive analytics Real-time risk assessment models integrating IoT, behavioral, and external data Strategies for robust compliance and governance with operational efficiency Discover how data intelligence is revolutionizing insurance risk management and safeguarding the industry’s future.

This introductory workshop caters to data engineers seeking hands-on experience and data architects looking to deepen their knowledge. The workshop is structured to provide a solid understanding of the following data engineering and streaming concepts: Introduction to Lakeflow and the Data Intelligence Platform Getting started with Lakeflow Declarative Pipelines for declarative data pipelines in SQL using Streaming Tables and Materialized Views Mastering Databricks Workflows with advanced control flow and triggers Understanding serverless compute Data governance and lineage with Unity Catalog Generative AI for Data Engineers: Genie and Databricks Assistant We believe you can only become an expert if you work on real problems and gain hands-on experience. Therefore, we will equip you with your own lab environment in this workshop and guide you through practical exercises like using GitHub, ingesting data from various sources, creating batch and streaming data pipelines, and more.

Want to learn how to build your own custom data intelligence applications directly in Databricks? In this workshop, we’ll guide you through a hands-on tutorial for building a Streamlit web app that leverages many of the key products at Databricks as building blocks. You’ll integrate a live DB SQL warehouse, use Genie to ask questions in natural language, and embed AI/BI dashboards for interactive visualizations. In addition, we’ll discuss key concepts and best practices for building production-ready apps, including logging and observability, scalability, different authorization models, and deployment. By the end, you'll have a working AI app—and the skills to build more.

Deploying AI in production is getting more complex — with different model types, tighter timelines, and growing infrastructure demands. In this session, we’ll walk through how Mosaic AI Model Serving helps teams deploy and scale both traditional ML and generative AI models efficiently, with built-in monitoring and governance.We’ll also hear from Skyscanner on how they’ve integrated AI into their products, scaled to 100+ production endpoints, and built the processes and team structures to support AI at scale. Key Takeaways: How Skyscanner ships and operates AI in real-world products How to deploy and scale a variety of models with low latency and minimal overhead Building compound AI systems using models, feature stores, and vector search Monitoring, debugging, and governing production workloads