Join us to hear about how Epsilon Data Management migrated Epsilon’s unique, AI-powered marketing identity solution from multi-petabyte on-prem Hadoop and data warehouse systems to a unified Databricks Lakehouse platform. This transition enabled Epsilon to further scale its Decision Sciences solution and enable new cloud-based AI research capabilities on time and within budget, without being bottlenecked by the resource constraints of on-prem systems. Learn how Delta Lake, Unity Catalog, MLflow and LLM endpoints powered massive data volume, reduced data duplication, improved lineage visibility, accelerated Data Science and AI, and enabled new data to be immediately available for consumption by the entire Epsilon platform in a privacy-safe way. Using the Databricks platform as the base for AI and Data Science at global internet scale, Epsilon deploys marketing solutions across multiple cloud providers and multiple regions for many customers.

Many studies have indicated that having a strong Data & AI culture helps our businesses be more successful. This can lead to better business performance, becoming more profitable and being more competitive compared to your peer companies as well as attaining and retaining top talent. What does it mean to have a Data & AI culture? It’s the ability for an organization to make data-driven decisions. It means using insights to improve your business results and using data ultimately allows you to enable AI. It tends to be the people that get in the way of having and sustaining an effective Data & AI culture. Do you have people already in your teams that can help you build your Data & AI culture? Can you attract and retain that talent to your organization? Can you help integrate that great talent into your organization to promote a Data & AI culture? It’s also ensuring that you fundamentally change the way you/your teams/organizations work.

This session is repeated. Get the most out of your AI/BI Dashboards by scaling them across your entire organization. This session covers best practices for automating report distribution, embedding dashboards in external applications, and ensuring secure access across all surfaces. You'll walk away with practical strategies for delivering insights to the right people at the right time—empowering decision-makers at every level with the data they need to drive impactful outcomes.

Delta Kernel makes it easy for engines and connectors to read and write Delta tables. It supports many Delta features and robust connectors, including DuckDB, Clickhouse, Spice AI and delta-dotnet. In this session, we'll cover lessons learned about how to build a high-performance library that lets engines integrate the way they want, while not having to worry about the details of the Delta protocol. We'll talk through how we streamlined the API as well as its changes and underlying motivations. We'll discuss some new highlight features like write support, and the ability to do CDF scans. Finally we'll cover the future roadmap for the Kernel project and what you can expect from the project over the coming year.

Databricks Unity Catalog (UC) is the industry’s only unified and open governance solution for data and AI, built into the Databricks Data Intelligence Platform. UC provides a single source of truth for organization’s data and AI, providing open connectivity to any data source, any format, lineage, monitoring and support for open sharing and collaboration. In this session we will discuss the challenges in upgrading to UC from your existing databricks Non-UC set up. We will discuss a few customer use cases and how we overcame difficulties and created a repeatable pattern and reusable assets to replicate the success of upgrading to UC across some of the largest databricks customers. It is co-presented with our partner Celebal Technologies.

Join us to hear about how National Australia Bank (NAB) successfully completed a significant milestone in its data strategy by decommissioning its 26-year-old Teradata environment and migrating to a new strategic data platform called 'Ada'. This transition marks a pivotal shift from legacy systems to a modern, cloud-based data and AI platform powered by Databricks. The migration process, which spanned two years, involved ingesting 16 data sources, transferring 456 use cases, and collaborating with hundreds of users across 12 business units. This strategic move positions NAB to leverage the full potential of cloud-native data analytics, enabling more agile and data-driven decision-making across the organization. The successful migration to Ada represents a significant step forward in NAB's ongoing efforts to modernize its data infrastructure and capitalize on emerging technologies in the rapidly evolving financial services landscape

Customer support is going through the GenAI revolution, but how can we use AI to foster deeper empathy with our end users?To enable this, Earnin has built its GenAI observability platform on Databricks, leveraging Lakeflow Declarative Pipeliness, Kafka and Databricks AI/BI.This session covers how we use Lakeflow Declarative Pipelines to monitor our customer care chatbot in near real-time and how we leverage Databricks to better anticipate our customers' needs.

In this session, we will explore how Genie, an AI-driven platform transformed HVAC operational insights by leveraging Databricks offerings like Apache Spark, Delta Lake and the Databricks Data Intelligence Platform.Key contributions: Real-time data processing: Lakeflow Declarative Pipelines and Apache Spark™ for efficient data ingestion and real-time analysis. Workflow orchestration: Databricks Data Intelligence Platform to orchestrate complex workflows and integrate various data sources and analytical tools. Field Data Integration: Incorporating real-time field data into design and algorithm development, enabling engineers to make informed adjustments and optimize performance. By analyzing real-time data from HVAC installations, Genie identified discrepancies between design specs and field performance, allowing engineers to optimize algorithms, reduce inefficiencies and improve customer satisfaction. Discover how Genie revolutionized HVAC management and apply to your projects.

Ever wondered how industry leaders handle thousands of ML predictions per second? This session reveals the architecture behind high-performance model serving systems on Databricks. We'll explore how to build inference pipelines that efficiently scale to handle massive request volumes while maintaining low latency. You'll learn how to leverage Feature Store for consistent, low-latency feature lookups and implement auto-scaling strategies that optimize both performance and cost. Key takeaways: Determining optimal compute capacity using the QPS × model execution time formula Configuring Feature Store for high-throughput, low-latency feature retrieval Managing cold starts and scaling strategies for latency-sensitive applications Implementing monitoring systems that provide visibility into inference performance Whether you're serving recommender systems or real-time fraud detection models, you'll gain practical strategies for building enterprise-grade ML serving systems.

HP’s 3D Print division empowers manufacturers with telemetry data to optimize operations and streamline maintenance. Using Delta Sharing, Unity Catalog and AI/BI dashboards, HP provides a secure, scalable solution for data sharing and analytics. Delta Sharing D2O enables seamless data access, even for customers not on Databricks. Apigee masks private URLs, and Unity Catalog enhances security by managing data assets. Predictive maintenance with Mosaic AI boosts uptime by identifying issues early and alerting support teams. Custom dashboards and sample code let customers run analytics using any supported client, while Apigee simplifies access by abstracting complexity. Insights from A/BI dashboards help HP refines data strategy, aligning solutions with customer needs despite the complexity of diverse technologies, fragmented systems and customer-specific requirements. This fosters trust, drives innovation,and strengthens HP as a trusted partner for scalable, secure data solutions.

The Databricks Security team led a broad working group that significantly evolved the Databricks AI Security Framework (DASF) to its 2.0 version since its first release by closely collaborating with the top cyber security researchers at industry organizations such as OWASP, Gartner, NIST, HITRUST, FAIR Institute and several Fortune 100 companies to address the evolving risks and associated controls of AI systems in enterprises. Join us to to learn how The CLEVER GenAI pipeline, an AI-driven innovation in healthcare, processes over 1.5 million clinical notes daily to classify social determinants impacting veteran care while adhering to robust security measures like NIST 800-53 controls and by leveraging Databricks AI Security Framework. We will discuss robust AI security guidelines to help data and AI teams understand how to deploy their AI applications securely. This session will give a security framework for security teams, AI practitioners, data engineers and governance teams.

The AI Screening Agent automates Level 1 (L1) screening process, essential for Know Your Customer (KYC) and compliance due diligence during customer onboarding. This system aims to minimize false positives, significantly reducing human review time and costs. Beyond typical Retrieval-Augmented Generation (RAG) applications like summarization and chat-with-your-data (CWYD), the AI Screening Agent employs a ReAct architecture with intelligent tools, enabling it to perform complex compliance decision-making with human-like accuracy and greater consistency. In this talk, I will explore the screening agent architecture, demonstrating its ability to meet evolving client policies. I will discuss evaluation and configuration management using MLflow LLM-as-judge and Unity Catalog, and discuss challenges, such as, data fidelity and customization. This session underscores the transformative potential of AI agents in compliance workflows, emphasizing their adaptability, accuracy, and consistency.

At ServiceNow, we’re not just talking about AI innovation — we’re delivering it. By harnessing the power of Databricks, we’re reimagining Go-To-Market (GTM) strategies, seamlessly integrating AI at every stage of the deal journey — from identifying high-value leads to generating hyper-personalized outreach and pitch materials. In this session, learn how we’ve slashed data processing times by over 90%, reducing workflows from an entire day to just 30 minutes with Databricks. This unprecedented speed enables us to deploy AI-driven GTM initiatives faster, empowering our sellers with real-time insights that accelerate deal velocity and drive business growth. As Agentic AI becomes a game-changer in enterprise GTM, ServiceNow and Databricks are leading the charge — paving the way for a smarter, more efficient future in AI-powered sales.

In this talk, we will explore the transformative potential of Generative AI and Agentic AI in driving enterprise-scale innovation and delivering substantial business value. As organizations increasingly recognize the power of AI to move beyond automation towards true augmentation and intelligent decision-making, understanding the nuances of scaling these advanced AI paradigms becomes critical. We will delve into practical strategies for deploying, managing, and optimizing Agentic AI frameworks showcasing how autonomous, goal-directed AI systems can unlock new efficiencies, enhance customer experiences, and foster continuous innovation. Through real-world case studies and actionable insights, attendees will gain a comprehensive understanding of the key considerations to architect, implement, and measure the ROI of large-scale Generative and Agentic AI initiatives, positioning their enterprises for sustained growth and competitive advantage in the AI-first era.

Deloitte is observing a growing trend among cybersecurity organizations to develop big data management and analytics solutions beyond traditional Security Information and Event Management (SIEM) systems. Leveraging Databricks to extend these SIEM capabilities, Deloitte can help clients lower the cost of cyber data management while enabling scalable, cloud-native architectures. Deloitte helps clients design and implement cybersecurity data meshes, using Databricks as a foundational data lake platform to unify and govern security data at scale. Additionally, Deloitte extends clients’ cybersecurity capabilities by integrating advanced AI and machine learning solutions on Databricks, driving more proactive and automated cybersecurity solutions. Attendees will gain insight into how Deloitte is utilizing Databricks to manage enterprise cyber risks and deliver performant and innovative analytics and AI insights that traditional security tools and data platforms aren’t able to deliver.

Still coding data transformations by hand? Struggling with rigid, proprietary data prep tools? AI agents are flipping the script, reshaping data teams and delivering production-ready data preparation. Join this session to see how analysts, data scientists, and data engineers can build powerful, production-ready data pipelines simply by describing their intent in natural language. All in under 7 minutes. No complex UI or coding is required. Select datasets, join tables, apply filters, perform calculations - all just by chatting - and watch the pipeline materialize in real time, ready for deployment with documentation, testing, lineage, and versioning. Ready to leave slow, traditional data prep behind and be part of the next wave of innovation? You won’t want to miss this session.

Getting started with data and AI governance in the modern data stack? Unity Catalog is your gateway to secure, discoverable and well-governed data and AI assets. In this session, we’ll break down what Unity Catalog is, why it matters and how it simplifies access control, lineage, discovery, auditing, business semantics and secure, open collaboration — all from a single place. We’ll explore how it enables open interoperability across formats, tools and platforms, helping you avoid lock-in and build on open standards. Most importantly, you’ll learn how Unity Catalog lays the foundation for data intelligence — by unifying governance across data and AI, enabling AI tuned to your business. It helps build a deep understanding of your data and delivers contextual, domain-specific insights that boost productivity for both technical and business users across any workload.

In this session, you’ll see how to build and deploy a GenAI agent and Model Context Protocol (MCP) with Databricks, Anthropic, Mosaic External AI Gateway, and Amazon Bedrock. You will learn the architecture, best-practices of using Databricks Mosaic AI, Anthropic Sonnet 3.7 first-party frontier model, and LangGraph for custom workflow orchestration in Databricks Data Intelligence Platform. You’ll also see how to use Databricks Mosaic AI to provide agent evaluation and monitoring. In addition, you will also see how inline agent will use MCP to provide tools and other resources using Amazon Nova models with Amazon Bedrock inline agent for deep research. This approach gives you the flexibility of LangGraph, the powerful managed agents offered by Amazon Bedrock, and Databricks Mosaic AI’s operational support for evaluation and monitoring.

Join us for a compelling forum exploring how energy leaders are harnessing data and AI to build a more sustainable future. As the industry navigates the complex balance between rising global energy demands and ambitious decarbonization goals, innovative companies are discovering that intelligence-driven operations are the key to success. From optimizing renewable energy integration to revolutionizing grid management, learn how energy pioneers are using AI to transform traditional operations while accelerating the path to net zero. This session reveals how Databricks is empowering energy companies to turn their sustainability aspirations into reality, proving that the future of energy is both clean and intelligent.

Join us for the Tech Industry Forum, formerly known as the Tech Innovators Summit, now part of Databricks Industry Experience. This session will feature keynotes, panels and expert talks led by top customer speakers and Databricks experts. Tech companies are pushing the boundaries of data and AI to accelerate innovation, optimize operations and build collaborative ecosystems. In this session, we’ll explore how unified data platforms empower organizations to scale their impact, democratize analytics across teams and foster openness for building tomorrow’s products. Key topics include: Scaling data platforms to support real-time analytics and AI-driven decision-making Democratizing access to data while maintaining robust governance and security Harnessing openness and portability to enable seamless collaboration with partners and customers After the session, connect with your peers during the exclusive Industry Forum Happy Hour. Reserve your seat today!