Be first to witness the latest breakthroughs from Databricks and share the success of innovative data and AI companies.

Josue is well known for his practical perspectives on the data and AI landscape. We'll talk about what he is seeing in the market, his take on product feature updates, and some humor mixed in.

Each year at Summit, Women in Data and AI have a half day for in-person discussions on empowering Women in Data and AI Breakfast, and networking with like-minded professionals and trailblazers. For this virtual discussion, hear from Kate Ostbye (Pfizer), Lisa Cohen (Anthropic), Pallavi Koppol and Holly Smith (Databricks) about navigating challenges, celebrating successes, and inspire one another as we champion diversity and innovation in data together. And how to get involved year-round.

As companies increasingly adopt Generative AI, they're faced with a new challenge: managing multiple AI assistants. What if you could have a single, intuitive interface that automatically directs questions to the best assistant for the task? Join us to discover how to implement a flexible Routing Agent that streamlines working with multiple AI Assistants. We'll show you how to leverage Databricks and DSPy 3.0 to simplify adding this powerful pattern to your system. We'll dive into the essential aspects including: Using DSPy optimizers to maximize correct route selections Optimizing smaller models to reduce latency Creating stateful interactions Designing for growth and adaptability to support tens or hundreds of AI Assistants Ensuring authorized access to AI Assistants Tracking performance in production environments We'll share real-world examples that you can apply today. You'll leave with the knowledge to make your AI system run smoothly and efficiently.

While modern lakehouse architectures and open-table formats provide flexibility, they are often challenging to manage. Data layouts, clustering, and small files need to be managed for efficiency. Qbeast’s platform-independent patented muti-column indexing optimizes lakehouse data layout, accelerates queries, and sharply reduces compute cost — without disrupting existing architectures. Qbeast also handles high-cardinality clustering and supports incremental updates. Join us to explore how Qbeast enables efficient, scalable, AI-ready data infrastructure — reducing compute costs independent of data platform and compute engine.

Government leaders overwhelmingly recognize the potential benefits of AI as critical to long-term strategic goals of efficiency, but implementation challenges and security concerns could be obstacles to success.

Is your AI evaluation process holding back your system's true potential? Many organizations struggle with improving GenAI quality because they don't know how to measure it effectively. This research session covers the principles of GenAI evaluation, offers a framework for measuring what truly matters, and demonstrates implementation using Databricks.Key Takeaways:-Practical approaches for establishing reliable metrics for subjective evaluations-Techniques for calibrating LLM judges to enable cost-effective, scalable assessment-Actionable frameworks for evaluation systems that evolve with your AI capabilitiesWhether you're developing models, implementing AI solutions, or leading technical teams, this session will equip you to define meaningful quality metrics for your specific use cases and build evaluation systems that expose what's working and what isn't, transforming AI guesswork into measurable success.

Taxonomy generation is a challenge across industries such as retail, manufacturing and e-commerce. Incomplete or inconsistent taxonomies can lead to fragmented data insights, missed monetization opportunities and stalled revenue growth. In this session, we will explore a modern approach to solving this problem by leveraging Databricks platform to build a scalable compound AI architecture for automated taxonomy generation. The first half of the session will walk you through the business significance and implications of taxonomy, followed by a technical deep dive in building an architecture for taxonomy implementation on the Databricks platform using a compound AI architecture. We will walk attendees through the anatomy of taxonomy generation, showcasing an innovative solution that combines multimodal and text-based LLMs, internal data sources and external API calls. This ensemble approach ensures more accurate, comprehensive and adaptable taxonomies that align with business needs.

The insurance industry is at the crossroads of digital transformation, facing challenges from market competition and customer expectations. While conventional ML applications have historically provided capabilities in this domain, the emergence of Agentic AI frameworks presents a revolutionary opportunity to build truly autonomous insurance applications. We will address issues related to data governance and quality while discussing how to monitor/evaluate fine-tune models. We'll demonstrate the application of the agentic framework in the insurance context and how these autonomous agents can work collaboratively to handle complex insurance workflows — from submission intake and risk evaluation to expedited quote generation. This session demonstrates how to architect intelligent insurance solutions using Databricks Mosaic AI agentic core components including Unity Catalog, Playground, model evaluation/guardrails, privacy filters, AI functions and AI/BI Genie.

This session explains how we've made our Apache Spark™ versionless for end users by introducing a stable client API, environment versioning and automatic remediation. These capabilities have enabled auto-upgrade of hundreds of millions of workloads with minimal disruption for Serverless Notebooks and Jobs. We'll also introduce a new approach to dependency management using environments. Admins will learn how to speed up package installation with Default Base Environments, and users will see how to manage custom environments for their own workloads.

Modern data organizations have moved beyond big data analytics to also incorporate advanced AI/ML data workloads. These workflows often involve multimodal datasets containing documents, images, long-form text, embeddings, URLs and more. Unity Catalog is an ideal solution for organizing and governing this data at scale. When paired with the Daft open source data engine, you can build a truly multimodal, AI-ready data lakehouse. In this session, we’ll explore how Daft integrates with Unity Catalog’s core features (such as volumes and functions) to enable efficient, AI-driven data lakehouses. You will learn how to ingest and process multimodal data (images, text and videos), run AI/ML transformations and feature extractions at scale, and maintain full control and visibility over your data with Unity Catalog’s fine-grained governance.

In enterprise AI, Evaluation-Driven Development (EDD) ensures reliable, efficient systems by embedding continuous assessment and improvement into the AI development lifecycle. High-quality evaluation datasets are created using techniques like document analysis, synthetic data generation via Mosaic AI’s synthetic data generation API, SME validation, and relevance filtering, reducing manual effort and accelerating workflows. EDD focuses on metrics such as context relevance, groundedness, and response accuracy to identify and address issues like retrieval errors or model limitations. Custom LLM judges, tailored to domain-specific needs like PII detection or tone assessment, enhance evaluations. By leveraging tools like Mosaic AI Agent Framework and Agent Evaluation, MLflow, EDD automates data tracking, streamlines workflows, and quantifies improvements, transforming AI development for delivering scalable, high-performing systems that drive measurable organizational value.

I have metrics, you have metrics — we all have metrics. But the real problem isn’t having metrics, it’s that the numbers never line up, leading to endless cycles of reconciliation and confusion. Join us as we share how our Data Team at Databricks tackled this fundamental challenge in Business Intelligence by building an internal Metric Store — creating a single source of truth for all business metrics using the newly-launched UC Metric Views. Imagine a world where numbers always align, metric definitions are consistently applied across the organization and every metric comes with built-in ML-based forecasting, AI-powered anomaly detection and automatic explainability. That’s the future we’ve built — and we’ll show you how you can get started today.

Discover how the latest innovations in Databricks AI/BI Dashboards and Genie are transforming self-service analytics. This session offers a high-level tour of new capabilities that empower business users to ask questions in natural language, generate insights faster and make smarter decisions. Whether you're a long-time Databricks user or just exploring what's possible with AI/BI, you'll walk away with a clear understanding of how these tools are evolving — and how to leverage them for greater business impact.

Join two energy industry leaders as they showcase groundbreaking applications of AI and data solutions in modern oil and gas operations. NOV demonstrates how their Generative AI pipeline revolutionized drilling mud report processing, automating the analysis of 300 reports daily with near-perfect accuracy and real-time analytics capabilities. BP shares how Unity Catalog has transformed their enterprise-wide data strategy, breaking down silos while maintaining robust governance and security. Together, these case studies illustrate how AI and advanced analytics are enabling cleaner, more efficient energy operations while maintaining the reliability demanded by today's market.

The insurance industry is rapidly evolving as advances in data and artificial intelligence (AI) drive innovation, enabling more personalized customer experiences, streamlined operations, and improved efficiencies. With powerful data analytics and AI-driven solutions, insurers can automate claims processing, enhance risk management, and make real-time decisions. Leveraging insights from large and complex datasets, organizations are delivering more customer-centric products and services than ever before. Key takeaways: Real-world applications of data and AI in claims automation, underwriting, and customer engagementHow predictive analytics and advanced data modeling help anticipate risks and meet customer needs. Personalization of policies, optimized pricing, and more efficient workflows for greater ROI. Discover how data and AI are fueling growth, improving protection, and shaping the future of the insurance industry!

Learn from the experts on how Databricks’ Mosaic AI Model Serving delivers unparalleled speed and scalability for deploying AI models. This session delves into the architecture and innovations that showcase the impressive improvements in throughput for the AI-serving infrastructure that powers Mosaic AI.

Bringing AI/BI to every business user starts with getting security, access and governance right. In this session, we’ll walk through the latest best practices for configuring Databricks accounts, setting up workspaces, and managing authentication protocols to enable secure and scalable onboarding. Whether you're supporting a small team or an entire enterprise, you'll gain practical insights to protect your data while ensuring seamless and governed access to AI/BI tools.

LLM agents often drift into failure when prompts, retrieval, external data, and policies interact in unpredictable ways. This technical session introduces a repeatable, metric-driven framework for detecting, diagnosing, and correcting these undesirable behaviors in agentic systems at production scale. We demonstrate how to instrument the agent loop with fine-grained signals—tool-selection quality, error rates, action progression, latency, and domain-specific metrics—and send them into an evaluation layer (e.g. Galileo). This telemetry enables a virtuous cycle of system improvement. We present a practical example of a stock-trading system and show how brittle retrieval and faulty business logic cause undesirable behavior. We refactor prompts, adjust the retrieval pipeline—verifying recovery through improved metrics. Attendees will learn how to: add observability with minimal code change, pinpoint root causes via tracing, and drive continuous, metric-validated improvement.

In a landscape where customer expectations are evolving faster than ever, the ability to activate real-time, first-party data is becoming the difference between reactive and intelligent businesses. This fireside chat brings together experts from Capgemini, Twilio Segment, and leading marketplace StockX to explore how organizations are building future-proof data foundations that power scalable, responsible AI.