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.

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.

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.

Consumer-facing industries are evolving faster than ever — and in today’s competitive landscape, it’s supply chains, not companies, that are truly competing. While data and AI offer huge potential for optimization, many organizations struggle to turn use cases into real business impact. In this session, leaders from retail, consumer goods, travel and hospitality will share how they’re building strong data foundations to unlock AI-driven supply chain optimization. Learn how they're using generative AI to boost productivity, streamline operations and improve insights through better data collaboration.

In an era where cloud costs can spiral out of control, Sportsbet achieved a remarkable 49% reduction in Total Cost of Ownership (TCO) through an innovative AI-powered solution called 'Kill Bill.' This presentation reveals how we transformed Databricks' consumption-based pricing model from a challenge into a strategic advantage through an intelligent automation and optimization. Understand how to use GenAI to reduce Databricks TCO Leverage generative AI within Databricks solutions enables automated analysis of cluster logs, resource consumption, configurations, and codebases to provide Spark optimization suggestions Create AI agentic workflows by integrating Databricks' AI tools and Databricks Data Engineering tools Review a case study demonstrating how Total Cost of Ownership was reduced in practice. Attendees will leave with a clear understanding of how to implement AI within Databricks solutions to address similar cost challenges in their environments.

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.

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 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.

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.

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.

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.

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.

In this session, discover how effective data movement is foundational to successful GenAI implementations. As organizations rush to adopt AI technologies, many struggle with the infrastructure needed to manage the massive influx of unstructured data these systems require. Jim Kutz, Head of Data at Airbyte, draws from 20+ years of experience leading data teams at companies like Grafana, CircleCI, and BlackRock to demonstrate how modern data movement architectures can enable secure, compliant GenAI applications. Learn practical approaches to data sovereignty, metadata management, and privacy controls that transform data governance into an enabler for AI innovation. This session will explore how you can securely leverage your most valuable asset—first-party data—for GenAI applications while maintaining complete control over sensitive information. Walk away with actionable strategies for building an AI-ready data infrastructure that balances innovation with governance requirements.

As insurers increasingly leverage IoT data to personalize policy pricing, reconciling disparate datasets across devices, policies, and insurers becomes mission-critical. In this session, learn how Nationwide transitioned from prototype workflows in Dataiku to a hardened data stack on Databricks, enabling scalable data governance and high-impact analytics. Discover how the team orchestrates data reconciliation across Postgres, Oracle, and Databricks to align customer driving behavior with insurer and policy data—ensuring more accurate, fair discounts for policyholders. With Anomalo’s automated monitoring layered on top, Nationwide ensures data quality at scale while empowering business units to define custom logic for proactive stewardship. We’ll also look ahead to how these foundations are preparing the enterprise for unstructured data and GenAI initiatives.

Go beyond the user interface and explore the cutting-edge technology driving AI/BI Genie. This session breaks down the AI/BI Genie architecture, showcasing how LLMs, retrieval-augmented generation (RAG) and finely tuned knowledge bases work together to deliver fast, accurate responses. We’ll also explore how AI agents orchestrate workflows, optimize query performance and continuously refine their understanding. Ideal for those who want to geek out about the tech stack behind Genie, this session offers a rare look at the magic under the hood.

Generic LLM metrics are useless until it meets your business needs.In this session we will dive deep into creating bespoke custom state-of-the-art AI metrics that matters to you. Discuss best practices on LLM evaluation strategies, when to use LLM judge vs. statistical metrics and many more. Through a live demo using Mosaic AI Framework, we will showcase: How you can build your own custom AI metric tailored to your needs for your GenAI application Implement autonomous AI evaluation suite for complex, multi-agent systems Generate ground truth data at scale and production monitoring strategies Drawing from extensive experience on working with customers on real-world use cases, we will share actionable insights on building a robust AI evaluation framework By the end of this session, you'll be equipped to create AI solutions that are not only powerful but also relevant to your organizations needs. Join us to transform your AI strategy and make a tangible impact on your business!

Erste Group's transition to Azure Databricks marked a significant upgrade from a legacy system to a secure, scalable and cost-effective cloud platform. The initial architecture, characterized by a complex hub-spoke design and stringent compliance regulations, was replaced with a more efficient solution. The phased migration addressed high network costs and operational inefficiencies, resulting in a 60% reduction in networking costs and a 30% reduction in compute costs for the central team. This transformation, completed over a year, now supports real-time analytics, advanced machine learning and GenAI while ensuring compliance with European regulations. The new platform features a Unity Catalogue, separate data catalogs and dedicated workspaces, demonstrating a successful shift to a cloud-based machine learning environment with significant improvements in cost, performance and security.

Ensuring the operational excellence of AI agents in production requires robust monitoring capabilities that span both performance metrics and quality evaluation. This session explores Databricks' comprehensive Mosaic Agent Monitoring solution, designed to provide visibility into deployed AI agents through an intuitive dashboard that tracks critical operational metrics and quality indicators. We'll demonstrate how to use the Agent Monitoring solution to iteratively improve a production agent that delivers a better customer support experience while decreasing the cost of delivering customer support. We will show how to: Identify and proactively fix a quality problem with the GenAI agent’s response before it becomes a major issue. Understand user’s usage patterns and implement/test an feature improvement to the GenAI agent Key session takeaways include: Techniques for monitoring essential operational metrics, including request volume, latency, errors, and cost efficiency across your AI agent deployments Strategies for implementing continuous quality evaluation using AI judges that assess correctness, guideline adherence, and safety without requiring ground truth labels Best practices for setting up effective monitoring dashboards that enable dimension-based analysis across time periods, user feedback, and topic categories Methods for collecting and integrating end-user feedback to create a closed-loop system that drives iterative improvement of your AI agents