Deploying AI models efficiently and consistently is a challenge many organizations face. This session will explore how Vizient built a standardized MLOps stack using Databricks and Azure DevOps to streamline model development, deployment and monitoring. Attendees will gain insights into how Databricks Asset Bundles were leveraged to create reproducible, scalable pipelines and how Infrastructure-as-Code principles accelerated onboarding for new AI projects. The talk will cover: End-to-end MLOps stack setup, ensuring efficiency and governance CI/CD pipeline architecture, automating model versioning and deployment Standardizing AI model repositories, reducing development and deployment time Lessons learned, including challenges and best practices By the end of this session, participants will have a roadmap for implementing a scalable, reusable MLOps framework that enhances operational efficiency across AI initiatives.

In this deep-dive technical session, Ivan Trusov (Sr. SSA @ Databricks) and Giran Moodley (SA @ Databricks) — will explore the full-stack development of Databricks Apps, covering everything from frameworks to deployment. We’ll walk through essential topics, including: Frameworks & tooling — Pythonic (Dash, Streamlit, Gradio) vs. JS + Python stack Development lifecycle — Debugging, issue resolution and best practices Testing — Unit, integration and load testing strategies CI/CD & deployment — Automating with Databricks Asset Bundles Monitoring & observability — OpenTelemetry, metrics collection and analysis Expect a highly practical session with several live demos, showcasing the development loop, testing workflows and CI/CD automation. Whether you’re building internal tools or AI-powered products, this talk will equip you with the knowledge to ship robust, scalable Databricks Apps.

In this session, we will delve into the creation of an infrastructure, CI/CD processes and monitoring systems that facilitate the responsible and efficient deployment of Large Language Models (LLMs) at Intermountain Healthcare. Using the "AI Inventory Agents" project as a case study, we will showcase how an LLM Agent can assist in effort and impact estimates, as well as provide insights into various AI products, both custom-built and third-party hosted. This includes their responsible AI certification status, development status and monitoring status (lights on, performance, drift, etc.). Attendees will learn how to build and customize their own LLMOps infrastructure to ensure seamless deployment and monitoring of LLMs, adhering to responsible AI practices.

Think Databricks is just for data and models? Think again. In this session, you’ll see how to build and scale a full-stack AI app capable of handling thousands of queries per second entirely on Databricks. No extra cloud platforms, no patchwork infrastructure. Just one unified platform with native hosting, LLM integration, secure access, and built-in CI/CD. Learn how Databricks Apps, along with services like Model Serving, Jobs, and Gateways, streamline your architecture, eliminate boilerplate, and accelerate development, from prototype to production.

This in-depth session explores advanced MLOps practices for implementing production-grade machine learning workflows on Databricks. We'll examine the complete MLOps journey from foundational principles to sophisticated implementation patterns, covering essential tools including MLflow, Unity Catalog, Feature Stores and version control with Git. Dive into Databricks' latest MLOps capabilities including MLflow 3.0, which enhances the entire ML lifecycle from development to deployment with particular focus on generative AI applications. Key session takeaways include: Advanced MLflow 3.0 features for LLM management and deployment Enterprise-grade governance with Unity Catalog integration Robust promotion patterns across development, staging and production CI/CD pipeline automation for continuous deployment GenAI application evaluation and streamlined deployment

Building and deploying Pyspark pipelines to Databricks should be effortless. However, our team at FreeWheel has, for the longest time, struggled with a convoluted and hard-to-maintain CI/CD infrastructure. It followed an imperative paradigm, demanding that every project implement custom scripts to build artifacts and deploy resources, and resulting in redundant boilerplate code and awkward interactions with the Databricks REST API. We set our mind on rebuilding it from scratch, following a declarative paradigm instead. We will share how we were able to eliminate thousands of lines of code from our repository, create a fully configuration-driven infrastructure where projects can be easily onboarded, and improve the quality of our codebase using Hatch and Databricks Asset Bundles as our tools of choice. In particular, DAB has made deploying across our 3 environments a breeze, and has allowed us to quickly adopt new features as soon as they are released by Databricks.

Health Catalyst (HCAT) transformed its CI/CD strategy by replacing a rigid, internal deployment tool with Databricks Asset Bundles (DABs), unlocking greater agility and efficiency. This shift streamlined deployments across both customer workspaces and HCAT's core platform, accelerating time to insights and driving continuous innovation. By adopting DABs, HCAT ensures feature parity, standardizes metric stores across clients, and rapidly delivers tailored analytics solutions. Attendees will gain practical insights into modernizing CI/CD pipelines for healthcare analytics, leveraging Databricks to scale data-driven improvements. HCAT's next-generation platform, Health Catalyst Ignite™, integrates healthcare-specific data models, self-service analytics, and domain expertise—powering faster, smarter decision-making.

The Global Water Security Center translates environmental science into actionable insights for the U.S. Department of Defense. Prior to incorporating Databricks, responding to these requests required querying approximately five hundred thousand raster files representing over five hundred billion points. By leveraging lakehouse architecture, Databricks Auto Loader, Spark Streaming, Databricks Spatial SQL, H3 geospatial indexing and Databricks Liquid Clustering, we were able to drastically reduce our “time to analysis” from multiple business days to a matter of seconds. Now, our data scientists execute queries on pre-computed tables in Databricks, resulting in a “time to analysis” that is 99% faster, giving our teams more time for deeper analysis of the data. Additionally, we’ve incorporated Databricks Workflows, Databricks Asset Bundles, Git and Git Actions to support CI/CD across workspaces. We completed this work in close partnership with Databricks.

The role of data teams and data engineers is evolving. No longer just pipeline builders or dashboard creators, today’s data teams must evolve to drive business strategy, enable automation, and scale with growing demands. Best practices seen in the software engineering world (Agile development, CI/CD, and Infrastructure-as-code) from the DevOps movement are gradually making their way into data engineering. We believe these changes have led to the rise of DataOps and a new wave of best practices that will transform the discipline of data engineering. But how do you transform a reactive team into a proactive force for innovation? We’ll explore the key principles for building a resilient, high-impact data team—from structuring for collaboration, testing, automation, to leveraging modern orchestration tools. Whether you’re leading a team or looking to future-proof your career, you’ll walk away with actionable insights on how to stay ahead in the rapidly changing data landscape.

This course provides a comprehensive review of DevOps principles and their application to Databricks projects. It begins with an overview of core DevOps, DataOps, continuous integration (CI), continuous deployment (CD), and testing, and explores how these principles can be applied to data engineering pipelines. The course then focuses on continuous deployment within the CI/CD process, examining tools like the Databricks REST API, SDK, and CLI for project deployment. You will learn about Databricks Asset Bundles (DABs) and how they fit into the CI/CD process. You’ll dive into their key components, folder structure, and how they streamline deployment across various target environments in Databricks. You will also learn how to add variables, modify, validate, deploy, and execute Databricks Asset Bundles for multiple environments with different configurations using the Databricks CLI. Finally, the course introduces Visual Studio Code as an Interactive Development Environment (IDE) for building, testing, and deploying Databricks Asset Bundles locally, optimizing your development process. The course concludes with an introduction to automating deployment pipelines using GitHub Actions to enhance the CI/CD workflow with Databricks Asset Bundles. By the end of this course, you will be equipped to automate Databricks project deployments with Databricks Asset Bundles, improving efficiency through DevOps practices. Pre-requisites: Strong knowledge of the Databricks platform, including experience with Databricks Workspaces, Apache Spark, Delta Lake, the Medallion Architecture, Unity Catalog, Delta Live Tables, and Workflows. In particular, knowledge of leveraging Expectations with Lakeflow Declarative Pipelines. Labs : Yes Certification Path: Databricks Certified Data Engineer Professional

At Zillow, we have accelerated the volume and quality of our dashboards by leveraging a modern SDLC with version control and CI/CD. In the past three months, we have released 32 production-grade dashboards and shared them securely across the organization while cutting error rates in half over that span. In this session, we will provide an overview of how we utilize Databricks asset bundles and GitLab CI/CD to create performant dashboards that can be confidently used for mission-critical operations. As a concrete example, we'll then explore how Zillow's Data Platform team used this approach to automate our on-call support analysis, leveraging our dashboard development strategy alongside Databricks LLM offerings to create a comprehensive view that provides actionable performance metrics alongside AI-generated insights and action items from the hundreds of requests that make up our support workload.

As cybersecurity threats grow in volume and complexity, organizations must efficiently process security telemetry for best-in-class detection and mitigation. Barracuda’s XDR platform is redefining security operations by layering advanced detection methodologies over a broad range of supported technologies. Our vision is to deliver unparalleled protection through automation, machine learning and scalable detection frameworks, ensuring threats are identified and mitigated quickly. To achieve this, we have adopted Databricks as the foundation of our security analytics platform, providing greater control and flexibility while decoupling from traditional SIEM tools. By leveraging Lakeflow Declarative Pipelines, Spark Structured Streaming and detection-as-code CI/CD pipelines, we have built a real-time detection engine that enhances scalability, accuracy and cost efficiency. This session explores how Databricks is shaping the future of XDR through real-time analytics and cloud-native security.

Managing authentication effectively is key to securing your data platform. In this session, we’ll explore best practices from Databricks for overcoming authentication challenges, including token visibility, MFA/SSO, CI/CD token federation and risk containment. Discover how to map your authentication maturity journey while maximizing security ROI. We'll showcase new capabilities like access token reports for improved visibility, streamlined MFA implementation and secure SSO with token federation. Learn strategies to minimize token risk through TTL limits, scoped tokens and network policies. You'll walk away with actionable insights to enhance your authentication practices and strengthen platform security on Databricks.

This session is repeated.Databricks Asset Bundles (DABs) provide a way to use the command line to deploy and run a set of Databricks assets — like notebooks, Python code, Lakeflow Declarative Pipelines and workflows. To automate deployments, you create a deployment pipeline that uses the power of DABs along with other validation steps to ensure high quality deployments.In this session you will learn how to automate CI/CD processes for Databricks while following best practices to keep deployments easy to scale and maintain. After a brief explanation of why Databricks Asset Bundles are a good option for CI/CD, we will walk through a working project including advanced variables, target-specific overrides, linting, integration testing and automatic deployment upon code review approval. You will leave the session clear on how to build your first GitHub Action using DABs.ub Action using DABs.

Hundreds of customers are already ingesting data with Lakeflow Connect from SQL Server, Salesforce, ServiceNow, Google Analytics, SharePoint, PostgreSQL and more to unlock the full power of their data. Lakeflow Connect introduces built-in, no-code ingestion connectors from SaaS applications, databases and file sources to help unlock data intelligence. In this demo-packed session, you’ll learn how to ingest ready-to-use data for analytics and AI with a few clicks in the UI or a few lines of code. We’ll also demonstrate how Lakeflow Connect is fully integrated with the Databricks Data Intelligence Platform for built-in governance, observability, CI/CD, automated pipeline maintenance and more. Finally, we’ll explain how to use Lakeflow Connect in combination with downstream analytics and AI tools to tackle common business challenges and drive business impact.

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

Summary In this episode of the Data Engineering Podcast Jeremy Edberg, CEO of DBOS, about durable execution and its impact on designing and implementing business logic for data systems. Jeremy explains how DBOS's serverless platform and orchestrator provide local resilience and reduce operational overhead, ensuring exactly-once execution in distributed systems through the use of the Transact library. He discusses the importance of version management in long-running workflows and how DBOS simplifies system design by reducing infrastructure needs like queues and CI pipelines, making it beneficial for data pipelines, AI workloads, and agentic AI.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details.Your host is Tobias Macey and today I'm interviewing Jeremy Edberg about durable execution and how it influences the design and implementation of business logicInterview IntroductionHow did you get involved in the area of data management?Can you describe what DBOS is and the story behind it?What is durable execution?What are some of the notable ways that inclusion of durable execution in an application architecture changes the ways that the rest of the application is implemented? (e.g. error handling, logic flow, etc.)Many data pipelines involve complex, multi-step workflows. How does DBOS simplify the creation and management of resilient data pipelines? How does durable execution impact the operational complexity of data management systems?One of the complexities in durable execution is managing code/data changes to workflows while existing executions are still processing. What are some of the useful patterns for addressing that challenge and how does DBOS help?Can you describe how DBOS is architected?How have the design and goals of the system changed since you first started working on it?What are the characteristics of Postgres that make it suitable for the persistence mechanism of DBOS?What are the guiding principles that you rely on to determine the boundaries between the open source and commercial elements of DBOS?What are the most interesting, innovative, or unexpected ways that you have seen DBOS used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on DBOS?When is DBOS the wrong choice?What do you have planned for the future of DBOS?Contact Info LinkedInParting Question From your perspective, what is the biggest gap in the tooling or technology for data management today?Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links DBOSExactly Once SemanticsTemporalSempahorePostgresDBOS TransactPython Typescript Idempotency KeysAgentic AIState MachineYugabyteDBPodcast EpisodeCockroachDBSupabaseNeonPodcast EpisodeAirflowThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Build & deploy with Google Cloud Deploy! This hands-on lab equips you to create delivery pipelines, deploy container images to Artifact Registry, and promote applications across GKE environments.

If you register for a Learning Center lab, please ensure that you sign up for a Google Cloud Skills Boost account for both your work domain and personal email address. You will need to authenticate your account as well (be sure to check your spam folder!). This will ensure you can arrive and access your labs quickly onsite. You can follow this link to sign up!

Build & deploy with Google Cloud Deploy! This hands-on lab equips you to create delivery pipelines, deploy container images to Artifact Registry, and promote applications across GKE environments.

If you register for a Learning Center lab, please ensure that you sign up for a Google Cloud Skills Boost account for both your work domain and personal email address. You will need to authenticate your account as well (be sure to check your spam folder!). This will ensure you can arrive and access your labs quickly onsite. You can follow this link to sign up!

Discover how Renault transformed automotive software development (SDV) with Google Cloud. By replacing physical prototypes with Android-based virtualization, they accelerated their SDV life cycle and moved to a cloud-first, iterative approach. Learn how they leverage Cloud Workstations, Gemini Code Assist, and a continuous integration and continuous testing (CI/CT) pipeline powered by Google Kubernetes Engine and GitLab to boost developer productivity and bring new features to market faster.