AI in healthcare has a data problem. Fragmented data remains one of the biggest challenges, and bottlenecks the development and deployment of AI solutions across life sciences, payers, and providers. Legacy paper-driven workflows and fragmented technology perpetuate silos, making it difficult to create a comprehensive, real-time picture of patient health. Datavant is leveraging Databricks and AWS technology to solve this problem at scale. Through our partnership with Databricks, we are centralizing storage of clinical data from what is arguably the largest health data network so that we can transform it into structured, AI-ready data – and shave off 80 percent of the work of deploying a new AI use case. Learn how we are handling the complexity of this effort while preserving the integrity of source data. We’ll also share early use cases now available to our healthcare customers.

The last major shift in data engineering came during the rise of the cloud, transforming how we store, manage, and analyze data. Today, we stand at the cusp of the next revolution: AI-driven data engineering. This shift promises not just faster pipelines, but a fundamental change in the way data systems are designed and maintained. AI will redefine who builds data infrastructure, automating routine tasks, enabling more teams to contribute to data platforms, and (if done right) freeing up engineers to focus on higher-value work. However, this transformation also brings heightened pressure around governance, risk, and data security, requiring new approaches to control and oversight. For those prepared, this is a moment of immense opportunity – a chance to embrace a future of smarter, faster, and more responsive data systems.

While Databricks powers your data lakehouse, DataHub delivers the critical context layer connecting your entire ecosystem. We'll demonstrate how DataHub extends Unity Catalog to provide comprehensive metadata intelligence across platforms. DataHub's real-time platform:Cut AI model time-to-market with our unified REST and GraphQL APIs that ensure models train on reliable and compliant data from across platforms, with complete lineage trackingDecrease data incidents by 60% using our event-driven architecture that instantly propagates changes across systems*Transform data discovery from days to minutes with AI-powered search and natural language interfaces.Leaders use DataHub to transform Databricks data into integrated insights that drive business value. See our demo of syncback technology—detecting sensitive data and enforcing Databricks access controls automatically—plus our AI assistant that enhances' LLMs with cross-platform metadata.

Data residency laws and legal mandates are driving the need for lakehouses across public and private clouds. This sprawl threatens centralized governance and compliance, while impacting cost, performance, and analytics/AI functionality. This session shows how e6data extends Unity Catalog across hybrid environments for consistent policy enforcement and query execution—regardless of data location—with guarantees around network egress, entitlements, performance, scalability, and cost. Learn how e6data’s “zero-data movement” philosophy powers a cost- and latency-optimized, location-aware architecture. We’ll cover onboarding strategies for hybrid fleets that enforce data movement restrictions and stay close to the data for better performance and lower cost. Discover how a location-aware compute strategy enables hybrid lakehouses with four key value metrics: cross-platform functionality, governed access, low latency, and total cost of ownership.

Enterprises need AI agents that are both powerful and production-ready while being scalable and secure. In this lightning session, you’ll learn how to leverage Retool’s platform and Databricks to design, deploy, and manage intelligent agents that automate complex workflows. We’ll cover best practices for integrating real-time Databricks data, enforcing governance, and ensuring scalability all while avoiding common pitfalls. Whether you’re automating internal ops or customer-facing tasks, walk away with a blueprint for shipping AI agents that actually work in the real world.

AI/BI Genie lets anyone simply talk with their own data, using natural language, fully secured through UC to provide accurate answers within the context for your organization. AI/BI Dashboards goes beyond traditional BI tools, democratizing everyone to self-serve immediate interactive visuals on your own secured data. Hear from a customer and Databricks experts on the latest developments.

Growth in capital markets thrives on innovation, agility and real-time insights. This session highlights how leading firms use Databricks’ Data Intelligence Platform to uncover opportunities, optimize trading strategies and deliver personalized client experiences. Learn how advanced analytics and AI help organizations expand their reach, improve decision-making and unlock new revenue streams. Industry leaders share how unified data platforms break down silos, deepen insights and drive success in a fast-changing market. Key takeaways: Predictive analytics and machine learning strategies for growth Real-world examples of optimized trading and enhanced client engagement Tools to innovate while ensuring operational efficiency Discover how data intelligence empowers capital markets firms to thrive in today’s competitive landscape!

Medical providers often receive less than 15 minutes of instruction in how to interact with patients during emotionally charged end of life interactions. Continuing education for clinicians is critical to hone these skills but is difficult to scale traditional approaches that require professional patients and instructors. Here, we describe a custom chatbot that plays the role of patient and coach to provide a scaling learning experience. A critical challenge was how to mitigate the persistently cheerful and helpful tone which results from standard pretraining in the Patient Persona AI. We accomplished this by implementing a multi-agent architecture based upon a graphical model of the conversation. System prompts reflecting the patient’s cognitive state are dynamically updated as the conversation progresses. Future extensions of the work are intended to focus on additional custom model fine-tuning in the Mosaic AI platform to further improve the realism of the conversation.

This session is repeated. In today’s data landscape, the challenge isn’t just storing or processing data — it’s enabling every user, from data stewards to analysts, to find and trust the right data, fast. This session explores how Databricks is reimagining data discovery with the new Discover Page Experience — an intuitive, curated interface showcasing key data and workspace assets. We’ll dive into AI-assisted governance and AI-powered discovery features like AI-generated metadata, AI-assisted lineage and natural language data exploration in Unity Catalog. Plus, see how new certifications and deprecations bring clarity to complex data environments. Whether you’re a data steward highlighting trusted assets or an analyst navigating data without deep schema knowledge, this session will show how Databricks is making data discovery seamless for everyone.

As Databricks transforms data processing, analytics and machine learning, managing platform costs has become crucial for organizations aiming to maximize value while staying within budget. While Databricks offers unmatched scalability and performance, inefficient usage can lead to unexpected cost overruns. This presentation will explore common challenges organizations face in controlling Databricks costs and provide actionable best practices for optimizing resource allocation, preventing over-provisioning and eliminating underutilization. Drawing from NTT DATA’s experience, I'll share how we reduced Databricks costs by up to 50% through strategies like choosing the right compute resource, leveraging manage tables and using Unity Catalog features, such as system tables, to monitor consumption. Join this session to gain practical insights and tools that will empower your team to optimize Databricks without overspending.

The implementation of the Databricks AI Factory enabled Seven West Media to transform its business by accelerating the launch of AI-driven use cases, fostering innovation and reducing time to market. By leveraging a unified data and AI platform, the company achieved better ROI through optimized workflows, improved operational efficiency and scalable machine learning models. The AI Factory empowered data teams to experiment faster, unlocking deeper audience insights that enhanced engagement and content personalization. This transformation positioned Seven West Media as a leader in AI-driven media, driving measurable business impact and future-proofing its data strategy.

Every analytics, BI and AI project relies on high-quality data. This is why data engineering, the practice of building reliable data pipelines that ingest and transform data, is consequential to the success of these projects. In this session, we'll show how you can use Lakeflow to accelerate innovation in multiple parts of the organization. We'll review real-world examples of Databricks customers using Lakeflow in different industries such as automotive, healthcare and retail. We'll touch on how the foundational data engineering capabilities Lakeflow provides help power initiatives that improve customer experiences, make real-time decisions and drive business results.

In this session, we’ll introduce Zerobus Direct Write API, part of Lakeflow Connect, which enables you to push data directly to your lakehouse and simplify ingestion for IOT, clickstreams, telemetry, and more. We’ll start with an overview of the ingestion landscape to date. Then, we'll cover how you can “shift left” with Zerobus, embedding data ingestion into your operational systems to make analytics and AI a core component of the business, rather than an afterthought. The result is a significantly simpler architecture that scales your operations, using this new paradigm to skip unnecessary hops. We'll also highlight one of our early customers, Joby Aviation and how they use Zerobus. Finally, we’ll provide a framework to help you understand when to use Zerobus versus other ingestion offerings—and we’ll wrap up with a live Q&A so that you can hit the ground running with your own use cases.

This session dives into building multi-agent systems on the Mosaic AI Platform, exploring the techniques, architectures and lessons learned from experiences building Greenlight’s real-world agent applications. This presentation is well suited for executives, product managers and engineers alike, breaking down AI Agents into easy-to-understand concepts, while presenting an architecture for building complex systems. We’ll examine the core components of generative AI Agents and different ways to assemble them into agents, including different prompting and reasoning techniques. We’ll cover how the Mosaic AI Platform has enabled our small team to build, deploy and monitor our AI Agents, touching on vector search, feature and model serving endpoints, and the evaluation framework. Finally, we’ll discuss the pros and cons of building a multi-agent system consisting of specialized agents vs. a single large agent for Greenlight’s AI Assistant, and the challenges we encountered.

Imagine performing complex regulatory checks in minutes instead of days. We made this a reality using GenAI on the Databricks Data Intelligence Platform. Join us for a deep dive into our journey from POC to a production-ready AI audit tool. Discover how we automated thousands of legal requirement checks in annual reports with remarkable speed and accuracy. Learn our blueprint for: High-Performance AI: Building a scalable, >90% accurate AI system with an optimized RAG pipeline that auditors praise. Robust Productionization: Achieving secure, governed deployment using Unity Catalog, MLflow, LLM-based evaluation, and MLOps best practices. This session provides actionable insights for deploying impactful, compliant GenAI in the enterprise.

This session is repeated. 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.

The convergence of cloud, data and AI is revolutionizing the pharmaceutical industry, creating a powerful ecosystem that drives innovation at scale across the entire value chain. At Gilead, teams harness these technologies on a unified cloud, data, & AI platform, accelerating business processes in pre-clinical and clinical stage, enabling smarter manufacturing and commercial processes, and deliver AI initiatives by reusing data products. Gilead will discuss how they have leveraged AWS, Databricks, and Data Mesh to manage vast amounts of heterogeneous data. Also, showcase use cases of traditional AI/ML, and Generative AI, and a Marketplace approach to drive adoption of AI Agents, demonstrating how this cloud-based, AI-powered platform is transforming the entire value chain. Gilead will also discuss how they are exploring the future of pharmaceutical innovation through Agentic AI, where the synergy of cloud, data and AI is unlocking new possibilities for a healthier world. In the second part, Muddu Sudhakar, Founder and Investor, will discuss how organizations can build and buy solutions for AI, Agents with Data Platforms. AWS and Databricks provide industry-leading platforms to build Agentic AI solutions. We will also cover Agentic AI Platform, Agent orchestration, Agent Interoperability, Agent Guardrails and Agentic workflows. This discussion also covers challenges in deploying and managing Agentic AI platforms. Enterprises need impactful AI initiatives & Agents to realize the promise and vision of AI and drive significant ROI.

Today’s organizations need faster, more reliable insights — but metric sprawl and inconsistent KPIs make that difficult. In this session, you’ll learn how Unity Catalog Metrics helps unify business semantics across your organization. Define your KPIs once, apply enterprise-grade governance with fine-grained access controls, auditing and lineage, and use them across any Databricks tool — from AI/BI Dashboards and Genie to notebooks and Lakeflow. You’ll learn how to eliminate metric chaos by centrally defining and governing metrics with Unity Catalog. You’ll walk away with strategies to boost trust through built-in governance and empower every team — regardless of technical skill — to work from the same certified metrics.

Take a front-row seat for a comprehensive, high-level introduction to Mosaic AI through the lens of Data Intelligence. In this session, we’ll spotlight the Databricks Platform’s newest features and announcements, showcase how Mosaic AI transforms raw enterprise data into actionable insights and share real-world examples of success. Whether you’re beginning your AI journey or scaling your existing efforts, this talk will provide you with the foundational knowledge and inspiration to fully leverage Mosaic AI for Data Intelligence and next-generation GenAI solutions.

Your data and AI use-cases are multiplying. At the same time, there is increased focus and scrutiny to meet sophisticated security and regulatory requirements. IQVIA utilizes serverless use-cases across data engineering, data analytics, and ML and AI, to empower their customers to make informed decisions, support their R&D processes and improve patient outcomes. By leveraging native controls on the platform, serverless enables them to streamline their use cases while maintaining a strong security posture, top performance and optimized costs. This session will go over IQVIA’s journey to serverless, how they met their security and regulatory requirements, and the latest and upcoming enhancements to the Databricks Platform.

As cheat developers evolve, so must detection techniques. This session will explore our methodologies, challenges and future directions, demonstrating how machine learning is transforming anti-cheat strategies and preserving competitive integrity in online gaming and how Databricks is enabling us to do so. As online gaming grows, maintaining fair play is an ongoing challenge. Call of Duty, a highly competitive first-person action game, faces aimbot usage—cheats that enable near-perfect accuracy, undermining fair play. Additionally, traditional detection methods are increasingly becoming less effective against advanced cheats that mimic human behavior. Machine learning presents a scalable and adaptive solution to this. We developed a data pipeline that collects features such as angle velocity, acceleration, etc. to train a deep neural network and deployed it. We are processing 30 million rows of data per hour for this detection on Databricks Platform.

Ready to streamline your ML lifecycle? Join us to explore MLflow 3.0 on Databricks, where we'll show you how to manage everything from experimentation to production with less effort and better results. See how this powerful platform provides comprehensive tracking, evaluation, and deployment capabilities for traditional ML models and cutting-edge generative AI applications. Key takeaways: Track experiments automatically to compare model performance Monitor models throughout their lifecycle across environments Manage deployments with robust versioning and governance Implement proven MLOps workflows across development stages Build and deploy generative AI applications at scale Whether you're an MLOps novice or veteran, you'll walk away with practical techniques to accelerate your ML development and deployment.

Learn how Data Profiling, Data Quality Monitoring, and Data Classification come together to provide end-to-end visibility into the health of your data and AI pipelines.

Discover how two industry leaders are delivering measurable business value through practical AI implementations. Lippert Components demonstrates their success in transforming customer support through GenAI, enhancing efficiency and reducing agent turnover across their million-call operation. Hypertherm shares how their innovative three-pronged automation approach revolutionized order processing, achieving 52% automation rates and handling 100,000 orders without human intervention in 2024, while freeing up valuable resources for strategic roles. These real-world applications showcase how AI solutions can drive operational excellence across customer service and supply chain domains.