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.

As autonomous agents become increasingly sophisticated and widely deployed, the ability for these agents to evaluate their own performance and continuously self-improve is essential. However, the growing complexity of these agents amplifies potential risks, including exposure to malicious inputs and generation of undesirable outputs. In this talk, we'll explore how to build resilient, self-improving agents. To drive self-improvement effectively, both the agent and the evaluation techniques must simultaneously improve with a continuously iterating feedback loop. Drawing from extensive real-world experiences across numerous productionized use cases, we will demonstrate practical strategies for combining tools from Arize, Databricks MLflow and Mosaic AI to evaluate and improve high-performing agents.

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.

Explore how to build use case-specific data products designed to power everything from traditional BI dashboards to machine learning and LLM-enabled applications. Gain an understanding of what data products are and why they are essential for delivering AI-ready data that is integrated, timely, high-quality, secure, contextual, and easily consumable. Discover strategies for unlocking business data from source systems to enable analytics and AI use cases, with a deep dive into the three-tiered data product architecture: the Data Product Engineering Plane (where data engineers ingest, integrate, and transform data), the Data Product Management Plane (where teams manage the full lifecycle of data products), and the Data Product Marketplace Plane (where consumers search for and use data products). Discover how a flexible, composable data architecture can support organizations at any stage of their data journey and drive impactful business outcomes.

Organizations face the challenge of managing vast amounts of data to combat emerging threats. The Databricks Data Intelligence platform represents a paradigm shift in cybersecurity at State Street, providing a comprehensive solution for managing and analyzing diverse security data. Through its partnership with Databricks, State Street has created a capability to: Efficiently manage structured and unstructured data. Scale up to analyze 50 petabytes of data in real-time. Ingest and parse data for critical security data streams. Build advanced cybersecurity data products and use automation & orchestration to streamline cybersecurity operations. By leveraging these capabilities, State Street has positioned itself as a leader in the financial services industry when it comes to cybersecurity.

Tired of waiting on SAP data? Join this session to see how Databricks and SAP make it easy to query business-ready data—no ETL. With Databricks SQL, you’ll get instant scale, automatic optimizations, and built-in governance across all your enterprise analytics data. Fast and AI-powered insights from SAP data are finally possible—and this is how.

Lakeflow brings much excitement, simplicity and unification to Databricks’ engineering experience. Databricks’ Bilal Aslam (Sr. Director of Product Management) and Josue A. Bogran (Databricks MVP & content creator) provide an overview of the history of Lakeflow, current value to your organization and the direction its capabilities are going toward. The session covers: What is Lakeflow? Differences and similarities between Lakeflow Declarative Pipelines Overview of current Lakeflow Connect, Pipelines and Jobs capabilities How to get started What's Next? The session will also provide you with an opportunity to ask questions to the team behind Lakeflow.

Building an AI-ready data platform requires strong governance, performance optimization, and seamless adoption of new technologies. At ThredUp, our Databricks journey began with a need for better data management and evolved into a full-scale transformation powering analytics, machine learning, and real-time decision-making. In this session, we’ll cover: Key inflection points: Moving from legacy systems to a modernized Delta Lake foundation Unity Catalog’s impact: Improving governance, access control, and data discovery Best practices for onboarding: Ensuring smooth adoption for engineering and analytics teams What’s next? Serverless SQL and conversational analytics with Genie Whether you’re new to Databricks or scaling an existing platform, you’ll gain practical insights on navigating the transition, avoiding pitfalls, and maximizing AI and data intelligence.

GovTech is an agency in the Singapore Government focused on tech for good. The GovTech Chief Data Office (CDO) has built the GovTech Data Platform with Databricks at the core. As the government tech agency, we safeguard national-level government and citizen data. A comprehensive data strategy is essential to uplifting data maturity. GovTech has adopted the service model approach where data services are offered to stakeholders based on their data maturity. Their maturity is uplifted through partnership, readying them for more advanced data analytics. CDO offers a plethora of data assets in a “data restaurant” ranging from raw data to data products, all delivered via Databricks and enabled through fine-grained access control, underpinned by data management best practices such as data quality, security and governance. Within our first year on Databricks, CDO was able to save 8,000 man-hours, democratize data across 50% of the agency and achieve six-figure savings through BI consolidation.

Industrial data is the foundation for operational excellence, but sharing and leveraging this data across systems presents significant challenges. Fragmented approaches create delays in decision-making, increase maintenance costs, and erode trust in data quality. This session explores how the partnership between AVEVA and Databricks addresses these issues through CONNECT, which integrates directly with Databricks via Delta Sharing. By accelerating time to value, eliminating data wrangling, ensuring high data quality, and reducing maintenance costs, this solution drives faster, more confident decision-making and greater user adoption. We will showcase how Agnico Eagle Mines—the world’s third-largest gold producer with 10 mines across Canada, Australia, Mexico, and Finland—is leveraging this capability to overcome data intelligence barriers at scale. With this solution, Agnico Eagle is making insights more accessible and actionable across its entire organization.

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.

The course is designed to cover advanced concepts and workflows in machine learning operations. It starts by introducing participants to continuous integration (CI) and continuous development (CD) workflows within machine learning projects, guiding them through the deployment of a sample CI/CD workflow using Databricks in the first section. Moving on to the second part, participants delve into data and model testing, where they actively create tests and automate CI/CD workflows. Finally, the course concludes with an exploration of model monitoring concepts, demonstrating the use of Lakehouse Monitoring to oversee machine learning models in production settings. Pre-requisites: Familiarity with Databricks workspace and notebooks; knowledge of machine learning model development and deployment with MLflow (e.g. intermediate-level knowledge of traditional ML concepts, development with CI/CD, the use of Python and Git for ML projects with popular platforms like GitHub) Labs: Yes Certification Path: Databricks Certified Machine Learning Professional

In this course, you’ll learn how to use the features Databricks provides for business intelligence needs: AI/BI Dashboards and AI/BI Genie. As a Databricks Data Analyst, you will be tasked with creating AI/BI Dashboards and AI/BI Genie Spaces within the platform, managing the access to these assets by stakeholders and necessary parties, and maintaining these assets as they are edited, refreshed, or decommissioned over the course of their lifespan. This course intends to instruct participants on how to design dashboards for business insights, share those with collaborators and stakeholders, and maintain those assets within the platform. Participants will also learn how to utilize AI/BI Genie Spaces to support self-service analytics through the creation and maintenance of these environments powered by the Databricks Data Intelligence Engine. Pre-requisites: The content was developed for participants with these skills/knowledge/abilities: A basic understanding of SQL for querying existing data tables in Databricks. Prior experience or basic familiarity with the Databricks Workspace UI. A basic understanding of the purpose and use of statistical analysis results. Familiarity with the concepts around dashboards used for business intelligence. Labs: Yes

In this course, you’ll learn how to define and schedule data pipelines that incrementally ingest and process data through multiple tables on the Data Intelligence Platform, using Lakeflow Declarative Pipelines in Spark SQL and Python. We’ll cover topics like how to get started with Lakeflow Declarative Pipelines, how Lakeflow Declarative Pipelines tracks data dependencies in data pipelines, how to configure and run data pipelines using the Lakeflow Declarative Pipelines. UI, how to use Python or Spark SQL to define data pipelines that ingest and process data through multiple tables on the Data Intelligence Platform, using Auto Loader and Lakeflow Declarative Pipelines, how to use APPLY CHANGES INTO syntax to process Change Data Capture feeds, and how to review event logs and data artifacts created by pipelines and troubleshoot syntax.By streamlining and automating reliable data ingestion and transformation workflows, this course equips you with the foundational data engineering skills needed to help kickstart AI use cases. Whether you're preparing high-quality training data or enabling real-time AI-driven insights, this course is a key step in advancing your AI journey.Pre-requisites: Beginner familiarity with the Databricks Data Intelligence Platform (selecting clusters, navigating the Workspace, executing notebooks), cloud computing concepts (virtual machines, object storage, etc.), production experience working with data warehouses and data lakes, intermediate experience with basic SQL concepts (select, filter, groupby, join, etc), beginner programming experience with Python (syntax, conditions, loops, functions), beginner programming experience with the Spark DataFrame API (Configure DataFrameReader and DataFrameWriter to read and write data, Express query transformations using DataFrame methods and Column expressions, etc.)Labs: NoCertification Path: Databricks Certified Data Engineer Associate

In this course, you’ll learn how to apply patterns to securely store and delete personal information for data governance and compliance on the Data Intelligence Platform. We’ll cover topics like storing sensitive data appropriately to simplify granting access and processing deletes, processing deletes to ensure compliance with the right to be forgotten, performing data masking, and configuring fine-grained access control to configure appropriate privileges to sensitive data.Pre-requisites: Ability to perform basic code development tasks using the Databricks workspace (create clusters, run code in notebooks, use basic notebook operations, import repos from git, etc), intermediate programming experience with SQL and PySpark (extract data from a variety of file formats and data sources, apply a number of common transformations to clean data, reshape and manipulate complex data using advanced built-in functions), intermediate programming experience with Delta Lake (create tables, perform complete and incremental updates, compact files, restore previous versions etc.). Beginner experience with Lakeflow Declarative Pipelines and streaming workloads.Labs: YesCertification Path: Databricks Certified Data Engineer Professional

This course is designed for data professionals who want to explore the data warehousing capabilities of Databricks. Assuming no prior knowledge of Databricks, it provides an introduction to leveraging Databricks as a modern cloud-based data warehousing solution. Learners will explore how use the Databricks Data Intelligence Platform to ingest, transform, govern, and analyze data efficiently. Learners will also explore Genie, an innovative Databricks feature that simplifies data exploration through natural language queries. By the end of this course, participants will be equipped with the foundational skills to implement and optimize a data warehouse using Databricks. Pre-requisites: Basic understanding of SQL and data querying concepts General knowledge of data warehousing concepts, including tables, schemas, and ETL/ELT processes is recommended Some experience with BI and/or data visualization tools is helpful but not required Labs: Yes

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

In this course, you’ll learn how to develop traditional machine learning models on Databricks. We’ll cover topics like using popular ML libraries, executing common tasks efficiently with AutoML and MLflow, harnessing Databricks' capabilities to track model training, leveraging feature stores for model development, and implementing hyperparameter tuning. Additionally, the course covers AutoML for rapid and low-code model training, ensuring that participants gain practical, real-world skills for streamlined and effective machine learning model development in the Databricks environment. Pre-requisites: Familiarity with Databricks workspace and notebooks, familiarity with Delta Lake and Lakehouse, intermediate level knowledge of Python (e.g. common Python libraries for DS/ML like Scikit-Learn, fundamental ML algorithms like regression and classification, model evaluation with common metrics) Labs: Yes Certification Path: Databricks Certified Machine Learning Associate

In this course, you will learn how to self-serve business insights from your company’s Databricks Data Intelligence Platform using AI/BI. After a tour of the fundamental components of the platform, you’ll learn how to interact with pre-created AI/BI Dashboards to explore your company’s data through existing charts and visualizations. You’ll also learn how to use AI/BI Genie to go beyond dashboards by asking follow-up questions in natural language to self-serve new insights, create visualizations, and share them with your colleagues. Pre-requisites: A working understanding of your organization’s business and key performance indicators. Labs: No Certification Path: N/A

In this course, you’ll learn how to optimize workloads and physical layout with Spark and Delta Lake and and analyze the Spark UI to assess performance and debug applications. We’ll cover topics like streaming, liquid clustering, data skipping, caching, photons, and more. Pre-requisites: Ability to perform basic code development tasks using the Databricks workspace (create clusters, run code in notebooks, use basic notebook operations, import repos from git, etc), intermediate programming experience with SQL and PySpark (extract data from a variety of file formats and data sources, apply a number of common transformations to clean data, reshape and manipulate complex data using advanced built-in functions), intermediate programming experience with Delta Lake (create tables, perform complete and incremental updates, compact files, restore previous versions etc.). Labs: Yes Certification Path: Databricks Certified Data Engineer Professional