This lightning talk dives into real-world GenAI projects that scaled from prototype to production using Databricks’ fully managed tools. Facing cost and time constraints, we leveraged four key Databricks features—Workflows, Model Serving, Serverless Compute, and Notebooks—to build an AI inference pipeline processing millions of documents (text and audiobooks). This approach enables rapid experimentation, easy tuning of GenAI prompts and compute settings, seamless data iteration and efficient quality testing—allowing Data Scientists and Engineers to collaborate effectively. Learn how to design modular, parameterized notebooks that run concurrently, manage dependencies and accelerate AI-driven insights. Whether you're optimizing AI inference, automating complex data workflows or architecting next-gen serverless AI systems, this session delivers actionable strategies to maximize performance while keeping costs low.

Agentic AI represents a quantum leap beyond generative AI—enabling systems to make autonomous decisions and act independently. While this unlocks transformative potential, it also brings complex governance challenges. This session explores novel risks, practical strategies and proven Data & AI governance frameworks for governing agentic AI at scale

In this session, we will share how we are transforming the way organizations process unstructured and non-standard documents using Mosaic AI and agentic patterns within the Databricks ecosystem. We have developed a scalable pipeline that turns complex legal and regulatory content into structured, tabular data.We will walk through the full architecture, which includes Unity Catalog for secure and governed data access, Databricks Vector Search for intelligent indexing and retrieval and Databricks Apps to deliver clear insights to business users. The solution supports multiple languages and formats, making it suitable for teams working across different regions. We will also discuss some of the key technical challenges we addressed, including handling parsing inconsistencies, grounding model responses and ensuring traceability across the entire process. If you are exploring how to apply GenAI and large language models, this session is for you. Audio for this session is delivered in the conference mobile app, you must bring your own headphones to listen.

Writing prompts for our GenAI applications is long, tedious, and unmaintainable. A proper software development lifecycle requires proper testing and maintenance, something incredibly difficult to do on a block of text. Our current prompt engineering best practices have largely been manual trial and error, testing which of our prompts work well in certain situations. This process worsens as our prompts become more complex, adding multiple tasks and functionality within one long singular prompt. Enter DSPy, your PROGRAMATIC way of building GenAI Applications. Learn how DSPy allows you to modularize your prompt into modules and enforce typing through signatures. Then, utilize state of the art algorithms to optimize the prompts and weights against your evaluation datasets, just like machine learning! We will compare DSPy to a restaurant to help illustrate and demo DSPy’s capabilities. It's time to start programming, rather than prompting, again!

Enterprise-grade GenAI needs a unified data strategy for accurate, reliable results. Learn how knowledge graphs make structured and unstructured data AI-ready while enabling governance and transparency. See how GraphRAG (retrieval-augmented generation with knowledge graphs) drives real success: Learn how companies like Klarna have deployed GenAI to build chatbots grounded in knowledge graphs, improving productivity and trust, while a major gaming company achieved 10x faster insights. We’ll share real examples and practical steps for successful GenAI deployment.

At Haleon, we've leveraged Databricks APIs and serverless compute to develop customer-facing applications for our business. This innovative solution enables us to efficiently deliver SAP invoice and order management data through front-end applications developed and served via our API Gateway. The Databricks lakehouse architecture has been instrumental in eliminating the friction associated with directly accessing SAP data from operational systems, while enhancing our performance capabilities. Our system acheived response times of less than 3 seconds from API call, with ongoing efforts to optimise this performance. This architecture not only streamlines our data and application ecosystem but also paves the way for integrating GenAI capabilities with robust governance measures for our future infrastructure. The implementation of this solution has yielded significant benefits, including a 15% reduction in customer service costs and a 28% increase in productivity for our customer support team.

Artificial Intelligence (AI) is more than a corporate tool; it’s a force for good. At Doctors Without Borders/Médecins Sans Frontières (MSF), we use AI to optimize fundraising, ensuring that every dollar raised directly supports life-saving medical aid worldwide. With Databricks, Mosaic AI and Unity Catalog, we analyze donor behavior, predict giving patterns and personalize outreach, increasing contributions while upholding ethical AI principles. This session will showcase how AI maximizes fundraising impact, enabling faster crisis response and resource allocation. We’ll explore predictive modeling for donor engagement, secure AI governance with Unity Catalog and our vision for generative AI in fundraising, leveraging AI-assisted storytelling to deepen donor connections. AI is not just about efficiency; it’s about saving lives. Join us to see how AI-driven fundraising is transforming humanitarian aid on a global scale.

This talk explores using advanced data processing and generative AI techniques to revolutionize the retail industry. Using Databricks, we will discuss how cutting-edge technologies enable real-time data analysis and machine learning applications, creating a powerful ecosystem for large-scale, data-driven retail solutions. Attendees will gain insights into architecting scalable data pipelines for retail operations and implementing advanced analytics on streaming customer data. Discover how these integrated technologies drive innovation in retail, enhancing customer experiences, streamlining operations and enabling data-driven decision-making. Learn how retailers can leverage these tools to gain a competitive edge in the rapidly evolving digital marketplace, ultimately driving growth and adaptability in the face of changing consumer behaviors and market dynamics.

At Italgas, Europe’s leading gas distributor both by network size and number of customers, we are spearheading digital transformation through a state-of-the-art, fully-fledged Databricks Intelligent platform. Achieved 50% cost reduction and 20% performance boost migrating from Azure Synapse to Databricks SQL Deployed 41 ML/GenAI models in production, with 100% of workloads governed by Unity Catalog Empowered 80% of employees with self-service BI through Genie Dashboards Enabled natural language queries for control-room operators analyzing network status The future of gas distribution is data-driven: predictive maintenance, automated operations, and real-time decision making are now realities. Our AI Factory isn't just digitizing infrastructure—it's creating a more responsive, efficient, and sustainable gas network that anticipates needs before they arise.

In this session, discover how National Australia Bank (NAB) is reshaping its data and AI strategy by positioning data as a strategic enabler. Driven by a vision to unlock data like electricity—continuous and reliable—NAB has established a scalable foundation for data intelligence that balances agility with enterprise-grade control. We'll delve into the key architectural, security, and governance capabilities underpinning this transformation, including Unity Catalog, Serverless, Lakeflow and GenAI. The session will highlight NAB's adoption of Databricks Serverless, platform security controls like private link, and persona-based data access patterns. Attendees will walk away with practical insights into building secure, scalable, and cost-efficient data platforms that fuel innovation while meeting the demands of compliance in highly regulated environments.

The last year has seen the rapid progress of Open Source GenAI models and frameworks. This talk covers best practices for custom training and OSS GenAI finetuning on Databricks, powered by the newly announced Serverless GPU Compute. We’ll cover how to use Serverless GPU compute to power AI training/GenAI finetuning workloads and framework support for libraries like LLM Foundry, Composer, HuggingFace, and more. Lastly, we’ll cover how to leverage MLFlow and the Databricks Lakehouse to streamline the end to end development of these models. Key takeaways include: How Serverless GPU compute saves customers valuable developer time and overhead when dealing with GPU infrastructure Best practices for training custom deep learning models (forecasting, recommendation, personalization) and finetuning OSS GenAI Models on GPUs across the Databricks stack Leveraging distributed GPU training frameworks (e.g. Pytorch, Huggingface) on Databricks Streamlining the path to production for these models Join us to learn about the newly announced Serverless GPU Compute and the latest updates to GPU training and finetuning on Databricks!

Join us to explore the dynamic partnership between FactSet and Databricks, transforming data accessibility and insights. Discover the launch of FactSet’s Structured DataFeeds via Delta Sharing on the Databricks Marketplace, enhancing access to crucial financial data insights. Learn about the advantages of streamlined data delivery and how this integration empowers data ecosystems. Beyond structured data, explore the innovative potential of vectorized data sharing of unstructured content such as news, transcripts, and filings. Gain insights into the importance of seamless vectorized data delivery to support GenAI applications and how FactSet is preparing to simplify client GenAI workflows with AI-ready data. Experience a demo that showcases the complete journey from data delivery to actionable GenAI application responses in a real-world Financial Services scenario. See firsthand how FactSet is simplifying client GenAI workflows with AI-ready data that drives faster, more informed financial decisions.

Timely and actionable insights are critical for staying competitive in today’s fast-paced environment. At HP Print, manual reporting for executive leadership (ELT) has been labor-intensive, hindering agility and productivity. To address this, we developed the Generative Insights Tool (GenIT) using Databricks Genie and Mosaic AI to create a real-time insights engine automating SQL generation, data visualization, and narrative creation. GenIT delivers instant insights, enabling faster decisions, greater productivity, and improved consistency while empowering leaders to respond to printer trends. With automated querying, AI-powered narratives, and a chatbot, GenIT reduces inefficiencies and ensures quality data and insights. Our roadmap integrates multi-modal data, enhances chatbot functionality, and scales globally. This initiative shows how HP Print leverages GenAI to improve decision-making, efficiency, and agility, and we will showcase this transformation at the Databricks AI Summit.

This session is repeated. Integrating AI agents into business systems requires tailored approaches for different maturity levels (crawl-walk-run) that balance scalability, accuracy and usability. This session addresses the critical challenge of making AI agents accessible to business users. We will explore four key integration methods: Databricks apps: The fastest way to build and run applications that leverage your data, with the full security and governance of Databricks Genie: Tool enabling non-technical users to gain data insights on Structured Data through natural language queries Chatbots: Combine real-time data retrieval with generative AI for contextual responses and process automation Batch inference: Scalable, asynchronous processing for large-scale AI tasks, optimizing efficiency and cost We'll compare these approaches, discussing their strengths, challenges and ideal use cases to help businesses select the most suitable integration strategy for their specific needs.

GenAI & machine learning are reshaping industries, driving innovation and redefining business strategies. As organizations embrace these technologies, they face significant challenges in managing AI initiatives effectively, such as balancing innovation with ethical integrity, operational resilience and regulatory compliance. This presentation introduces the Databricks AI Governance Framework (DAGF), a practical framework designed to empower organizations to navigate the complexities of AI. It provides strategies for building scalable, responsible AI programs that deliver measurable value, foster innovation and achieve long-term success. By examining the framework's five foundational pillars — AI organization, ethics, legal and regulatory compliance, transparency and interpretability, AI operations and infrastructure and AI security — this session highlights how AI governance aligns programs with the organization's strategic goals, mitigates risks and builds trust across stakeholders.

Swiggy, India's leading quick commerce platform, serves ~13 million users across 653 cities, with 196,000 restaurant partners and 17,000 SKUs. To handle this scale, Swiggy developed a secure, scalable AI platform processing millions of predictions per second. The tech stack includes Apache Kafka for real-time streaming, Apache Spark on Databricks for analytics and ML, and Apache Flink for stream processing. The Lakehouse architecture on Delta ensures data reliability, while Unity Catalog enables centralized access control and auditing. These technologies power critical AI applications like demand forecasting, route optimization, personalized recommendations, predictive delivery SLAs, and generative AI use cases.Key Takeaway:This session explores building a data platform at scale, focusing on cost efficiency, simplicity, and speed, empowering Swiggy to seamlessly support millions of users and AI use cases.

This course introduces learners to evaluating and governing GenAI (generative artificial intelligence) systems. First, learners will explore the meaning behind and motivation for building evaluation and governance/security systems. Next, the course will connect evaluation and governance systems to the Databricks Data Intelligence Platform. Third, learners will be introduced to a variety of evaluation techniques for specific components and types of applications. Finally, the course will conclude with an analysis of evaluating entire AI systems with respect to performance and cost. Pre-requisites: Familiarity with prompt engineering, and experience with the Databricks Data Intelligence Platform. Additionally, knowledge of retrieval-augmented generation (RAG) techniques including data preparation, embeddings, vectors, and vector databases Labs: Yes Certification Path: Databricks Certified Generative AI Engineer Associate

In this session, discover how Databricks leverages the power of Gen AI, MosaicML, Model Serving and Databricks Apps to revolutionize sales enablement. We’ll showcase how we built an advanced chatbot that equips our go-to-market team with the tools and knowledge needed to excel in customer-facing interactions. This AI-driven solution not only trains our salespeople but also enhances their confidence and effectiveness in demonstrating the transformative potential of Databricks to future customers. Attendees will gain insights into the architecture, development process and practical applications of this innovative approach. The session will conclude with an interactive demo, offering a firsthand look at the chatbot in action. Join us to explore how Databricks is using its own platform to drive sales excellence through cutting-edge AI solutions.

Discover how Xcel Energy and Lovelytics leveraged the power of geospatial analytics and GenAI to tackle one of the energy sector’s most pressing challenges—wildfire prevention. Transitioning from manual processes to automated GenAI unlocked transformative business value, delivering over 3x greater data coverage, over 4x improved accuracy, and 64x faster processing of geospatial data. In this session, you'll learn how Databricks empowers data leaders to transform raw data, like location information and visual imagery, into actionable insights that save costs, mitigate risks, and enhance customer service. Walk away with strategies for scaling geospatial workloads efficiently, building GenAI-driven solutions, and driving innovation in energy and utilities.

As data architectures evolve to meet the demands of real-time GenAI applications, organizations increasingly need systems that unify streaming and batch processing while maintaining compatibility with existing tools. The Ursa Engine offers a Kafka-API-compatible data streaming engine built on Lakehouse (Iceberg and Delta Lake). Designed to seamlessly integrate with data lakehouse architectures, Ursa extends your lakehouse capabilities by enabling streaming ingestion, transformation and processing — using a Kafka-compatible interface. In this session, we will explore how Ursa Engine augments your existing lakehouses with Kafka-compatible capabilities. Attendees will gain insights into Ursa Engine architecture and real-world use cases of Ursa Engine. Whether you're modernizing legacy systems or building cutting-edge AI-driven applications, discover how Ursa can help you unlock the full potential of your data.

Clinical Trial Data is undergoing a renaissance with new insights and data sources being added daily. The speed of new innovations and modalities that are found within trials poses an existential dilemma for 21CFR Part 11 compliance. In these validated environments, new components and methods need to be tested for reproducibility and restricted data access. In classical systems, this validation process would often have taken three months or more due to the manual validation process via validation scripts like Installation Qualification (IQ) and Operational Qualification (OQ) scripts. In conjunction with Databricks, Purgo AI has developed a new technology leveraging generative AI to automate the execution of IQ and OQ scripts and has drastically reduced the amount of time for validating Databricks from three months to less than a day. This drastic speedup of validation will enable the continuous flow of new ideas and implementations for clinical trials.

This course provides participants with information and practical experience in building advanced LLM (Large Language Model) applications using multi-stage reasoning LLM chains and agents. In the initial section, participants will learn how to decompose a problem into its components and select the most suitable model for each step to enhance business use cases. Following this, participants will construct a multi-stage reasoning chain utilizing LangChain and HuggingFace transformers. Finally, participants will be introduced to agents and will design an autonomous agent using generative models on Databricks. Pre-requisites: Solid understanding of natural language processing (NLP) concepts, familiarity with prompt engineering and prompt engineering best practices, experience with the Databricks Data Intelligence Platform, experience with retrieval-augmented generation (RAG) techniques including data preparation, building RAG architectures, and concepts like embeddings, vectors, and vector databases Labs: Yes Certification Path: Databricks Certified Generative AI Engineer Associate

This course is designed to introduce participants to contextual GenAI (generative artificial intelligence) solutions using the retrieval-augmented generation (RAG) method. Firstly, participants will be introduced to the RAG architecture and the significance of contextual information using Mosaic AI Playground. Next, the course will demonstrate how to prepare data for GenAI solutions and connect this process with building an RAG architecture. Finally, participants will explore concepts related to context embedding, vectors, vector databases, and the utilization of the Mosaic AI Vector Search product. Pre-requisites: Familiarity with embeddings, prompt engineering best practices, and experience with the Databricks Data Intelligence Platform Labs: Yes Certification Path: Databricks Certified Generative AI Engineer Associate