This course introduces learners to deploying, operationalizing, and monitoring generative artificial intelligence (AI) applications. First, learners will develop knowledge and skills in deploying generative AI applications using tools like Model Serving. Next, the course will discuss operationalizing generative AI applications following modern LLMOps best practices and recommended architectures. Finally, learners will be introduced to the idea of monitoring generative AI applications and their components using Lakehouse Monitoring. Pre-requisites: Familiarity with prompt engineering and retrieval-augmented generation (RAG) techniques, including data preparation, embeddings, vectors, and vector databases. A foundational knowledge of Databricks Data Intelligence Platform tools for evaluation and governance (particularly Unity Catalog). Labs: Yes Certification Path: Databricks Certified Generative AI Engineer Associate

This course will guide participants through a comprehensive exploration of machine learning model operations, focusing on MLOps and model lifecycle management. The initial segment covers essential MLOps components and best practices, providing participants with a strong foundation for effectively operationalizing machine learning models. In the latter part of the course, we will delve into the basics of the model lifecycle, demonstrating how to navigate it seamlessly using the Model Registry in conjunction with the Unity Catalog for efficient model management. By the course's conclusion, participants will have gained practical insights and a well-rounded understanding of MLOps principles, equipped with the skills needed to navigate the intricate landscape of machine learning model operations. Pre-requisites: Familiarity with Databricks workspace and notebooks, familiarity with Delta Lake and Lakehouse, intermediate level knowledge of Python (e.g. understanding of basic MLOps concepts and practices as well as infrastructure and importance of monitoring MLOps solutions) Labs: Yes Certification Path: Databricks Certified Machine Learning Associate

Adopting MLOps is getting increasingly important with the rise of AI. A lot of different features are required to do MLOps in large organizations. In the past, you had to implement these features yourself. Luckily, the MLOps space is getting more mature, and end-to-end platforms like Databricks provide most of the features. In this talk, I will walk through the MLOps components and how you can simplify your processes using Databricks. Audio for this session is delivered in the conference mobile app, you must bring your own headphones to listen.

At Exyte, we design, engineer, and deliver ultra-clean and sustainable facilities for high-tech industries. One of the most complex tasks our engineers and designers face is ensuring that their building designs comply with constantly evolving codes and regulations – often a manual, error-prone process. To address this, we developed ReguBIM AI, a generative AI-powered assistant that helps our teams verify code compliance more efficiently and accurately by linking 3D Building Information Modeling (BIM) data with regulatory documents. Built on the Databricks Data Intelligence Platform, ReguBIM AI is part of our broader vision to apply AI meaningfully across engineering and design processes. We are proud to share that ReguBIM AI won the Grand Prize and EMEA Winner titles at the Databricks GenAI World Cup 2024 — a global hackathon that challenged over 1,500 data scientists and AI engineers from 18 countries to create innovative generative AI solutions for real-world problems.

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.

Xoople aims to provide its users with trusted AI-Ready Earth data and accelerators that unlock new insights for enterprise AI. With access to scientific-grade Earth data that provides spatial intelligence on real-world changes, data scientists and BI analysts can increase forecast accuracy for their enterprise processes and models. These improvements drive smarter, data-driven business decisions across various business functions, including supply chain, finance, and risk across industries. Xoople, which has recently introduced their product, Enterprise AI-Ready Earth Data™, on the Databricks Marketplace, will have their CEO, Fabrizio Pirondini, discuss the importance of the Databricks Data Intelligence Platform in making Xoople’s product a reality for use in the enterprise.

Migrating legacy workloads to a modern, scalable platform like Databricks can be complex and resource-intensive. Impetus, an Elite Databricks Partner and the Databricks Migration Partner of the Year 2024, simplifies this journey with LeapLogic, an automated solution for data platform modernization and migration services. LeapLogic intelligently discovers, transforms, and optimizes workloads for Databricks, ensuring minimal risk and faster time-to-value. In this session, we’ll showcase real-world success stories of enterprises that have leveraged Impetus’ LeapLogic to modernize their data ecosystems efficiently. Join us to explore how you can accelerate your migration journey, unlock actionable insights, and future-proof your analytics with a seamless transition to Databricks.

Join this 20-minute session to learn how Informatica CDGC integrates with and leverages Unity Catalog metadata to provide end-to-end governance and security across an enterprise data landscape. Topics covered will include: Comprehensive data lineage that provides complete data transformation visibility across multicloud and hybrid environments -Broad data source support to facilitate holistic cataloging and a centralized governance framework Centralized access policy management and data stewardship to enable compliance with regulatory standards Rich data quality to ensure data is cleansed, validated and trusted for analytics and AI

AI is moving from pilots to production, but many organizations still struggle to connect boardroom ambitions with operational reality. Palantir’s Artificial Intelligence Platform (AIP) and the Databricks Data Intelligence Platform now form a single, open architecture that closes this gap by pairing Palantir’s operational decision empowering Ontology- with Databricks’ industry-leading scale, governance and Lakehouse economics. The result: real-time, AI-powered, autonomous workflows that are already powering mission-critical outcomes for the U.S. Department of Defense, bp and other joint customers across the public and private sectors. In this technically grounded but business-focused session you will see the new reference architecture in action. We will walk through how Unity Catalog and Palantir Virtual Tables provide governed, zero-copy access to Lakehouse data and back mission-critical operational workflows on top of Palantir’s semantic ontology and agentic AI capabilities. We will also explore how Palantir’s no-code and pro-code tooling integrates with Databricks compute to orchestrate builds and write tables to Unity Catalog. Come hear from customers currently using this architecture to drive critical business outcomes seamlessly across Databricks and Palantir.

This session is repeated. In this session, we present an overview of the GA release of Databricks Apps, the new app hosting platform that integrates all the Databricks services necessary to build production-ready data and AI applications. With Apps, data and developer teams can build new interfaces into the data intelligence platform, further democratizing the transformative power of data and AI across the organization. We'll cover common use cases, including RAG chat apps, interactive visualizations and custom workflow builders, as well as look at several best practices and design patterns when building apps. Finally, we'll look ahead with the vision, strategy and roadmap for the year ahead.

This session is repeated. In an era of exponential data growth, organizations across industries face common challenges in transforming raw data into actionable insights. This presentation showcases how Novo Nordisk is pioneering insights generation approaches to clinical data management and AI. Using our clinical trials platform FounData, built on Databricks, we demonstrate how proper data architecture enables advanced AI applications. We'll introduce a multi-agent AI framework that revolutionizes data interaction, combining specialized AI agents to guide users through complex datasets. While our focus is on clinical data, these principles apply across sectors – from manufacturing to financial services. Learn how democratizing access to data and AI capabilities can transform organizational efficiency while maintaining governance. Through this real-world implementation, participants will gain insights on building scalable data architectures and leveraging multi-agent AI frameworks for responsible innovation.

As a global energy leader, Petrobras relies on machine learning to optimize operations, but manual model deployment and validation processes once created bottlenecks that delayed critical insights. In this session, we’ll reveal how we revolutionized our MLOps framework using MLflow, Databricks Asset Bundles (DABs) and Unity Catalog to: Replace error-prone manual validation with automated metric-driven workflows Reduce model deployment timelines from days to hours Establish granular governance and reproducibility across production models Discover how we enabled data scientists to focus on innovation—not infrastructure—through standardized pipelines while ensuring compliance and scalability in one of the world’s most complex energy ecosystems.

As connected vehicles generate vast amounts of personal and sensitive data, ensuring privacy and security in machine learning (ML) processes is essential. This session explores how Trusted Execution Environments (TEEs) and Azure Confidential Computing can enable privacy-preserving ML in cloud environments. We’ll present a method to recreate a vehicle environment in the cloud, where sensitive data remains private throughout model training, inference and deployment. Attendees will learn how Mercedes-Benz R&D North America builds secure, privacy-respecting personalized systems for the next generation of connected vehicles.

This session is repeated. Peek behind the curtain to learn how Databricks processes hundreds of petabytes of data across every region and cloud where we operate. Learn how Databricks leverages Data and AI to scale and optimize every aspect of the company. From facilities and legal to sales and marketing and of course product research and development. This session is a high-level tour inside Databricks to see how Data and AI enable us to be a better company. We will go into the architecture of things for how Databricks is used for internal use cases like business analytics and SIEM as well as customer-facing features like system tables and assistant. We will cover how data production of our data flow and how we maintain security and privacy while operating a large multi-cloud, multi-region environment.

Analysts often begin their Databricks journey by running familiar SQL queries in the SQL Editor, but that’s just the start. In this session, I’ll share the roadmap I followed to expand beyond ad-hoc querying into SQL Editor/notebook-driven development to scheduled data pipelines producing interactive dashboards — all powered by Databricks SQL and Unity Catalog. You’ll learn how to organize tables with primary-key/foreign-key relationships along with creating table and column comments to form the semantic model, utilizing DBSQL features like RELY constraints. I’ll also show how parameterized dashboards can be set up to empower self-service analytics and feed into Genie Spaces. Attendees will walk away with best practices for starting out with building a robust BI platform on Databricks, including tips for table design and metadata enrichment. Whether you’re a data analyst or BI developer, this talk will help you unlock powerful, AI-enhanced analytics workflows.

Agentic AI is the next evolution in artificial intelligence, with the potential to revolutionize the industry. However, its potential is matched only by its risk: without high-quality, trustworthy data, agentic AI can be exponentially dangerous. Join Barr Moses, CEO and Co-Founder of Monte Carlo, to explore how to leverage Databricks' powerful platform to ensure your agentic AI initiatives are underpinned by reliable, high-quality data. Barr will share: How data quality impacts agentic AI performance at every stage of the pipeline Strategies for implementing data observability to detect and resolve data issues in real-time Best practices for building robust, error-resilient agentic AI models on Databricks. Real-world examples of businesses harnessing Databricks' scalability and Monte Carlo’s observability to drive trustworthy AI outcomes Learn how your organization can deliver more reliable agentic AI and turn the promise of autonomous intelligence into a strategic advantage.Audio for this session is delivered in the conference mobile app, you must bring your own headphones to listen.

From Overwhelmed to Empowered - How SAP is Democratizing Data & AI to Solve Real Business Problems with Databricks: Scaling the adoption of Data & AI within enterprises is critical for driving transformative business outcomes. Learn how the SAP Experience Garage, SAP’s largest internal enablement and innovation driver, is turning all employees into data enthusiast through the integration of Databricks technologies. The SAP Experience Garage platform brings together colleagues with various levels of data knowledge and skills in one seamless space. Here, they can explore and use tangible datasets and data science/AI tooling from Databricks, enablement capabilities, and collaborative features to tackle real business-related challenges and create prototypes that find their way into SAP’s ecosystem.

As AI, internal data marketplaces, and self-service access become more popular, data teams must rethink how they securely govern and provision data at scale. Success depends on provisioning data in a way that balances security, compliance, and innovation, and promotes data-driven decision making when decision makers are AI Agents. In this session, we'll discuss how you can:- Launch and manage effective and secure data provisioning- Secure your AI initiatives- Scale your Data Governors through Agentic AIJoin us to learn how to navigate the complexities of modern data environments, and start putting your data to work faster.

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

Scaling large language models (LLMs) and multimodal architectures requires efficient data management and computational power. NVIDIA NeMo Framework Megatron-LM on Databricks is an open source solution that integrates GPU acceleration and advanced parallelism with Databricks Delta Lakehouse, streamlining workflows for pre-training and fine-tuning models at scale. This session highlights context parallelism, a unique NeMo capability for parallelizing over sequence lengths, making it ideal for video datasets with large embeddings. Through the case study of TwelveLabs’ Pegasus-1 model, learn how NeMo empowers scalable multimodal AI development, from text to video processing, setting a new standard for LLM workflows.