Modernize your DevOps strategy with Agentic DevOps by migrating your Azure Repos to GitHub while continuing to leverage the investments you’ve made in Azure Boards and Azure Pipelines. We’ll walk through real-world patterns for hybrid adoption, show how to integrate GitHub, Azure Boards and Azure Pipelines, and share best practices for enabling agent-based workflows with the MCP Servers for Azure DevOps, Playwright and Azure.

Delivered in a silent stage breakout.

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 presentation, we'll show how we achieved a unified development experience for teams working on Mercedes-Benz Data Platforms in AWS and Azure. We will demonstrate how we implemented Azure to AWS and AWS to Azure data product sharing (using Delta Sharing and Cloud Tokens), integration with AWS Glue Iceberg tables through UniForm and automation to drive everything using Azure DevOps Pipelines and DABs. We will also show how to monitor and track cloud egress costs and how we present a consolidated view of all the data products and relevant cost information. The end goal is to show how customers can offer the same user experience to their engineers and not have to worry about which cloud or region the Data Product lives in. Instead, they can enroll in the data product through self-service and have it available to them in minutes, regardless of where it originates.

This session is repeated.Managing data and AI workloads in Databricks can be complex. Databricks Asset Bundles (DABs) simplify this by enabling declarative, Git-driven deployment workflows for notebooks, jobs, Lakeflow Declarative Pipelines, dashboards, ML models and more.Join the DABs Team for a Deep Dive and learn about:The Basics: Understanding Databricks asset bundlesDeclare, define and deploy assets, follow best practices, use templates and manage dependenciesCI/CD & Governance: Automate deployments with GitHub Actions/Azure DevOps, manage Dev vs. Prod differences, and ensure reproducibilityWhat’s new and what's coming up! AI/BI Dashboard support, Databricks Apps support, a Pythonic interface and workspace-based deploymentIf you're a data engineer, ML practitioner or platform architect, this talk will provide practical insights to improve reliability, efficiency and compliance in your Databricks workflows.

Learn about Watercare's journey in implementing a modern data stack with a focus on self serving analytics in the water industry. The session covers the reasons behind Watercare's decision to implement a modern data stack, the problem of data conformity, and the tools they used to accelerate their data modeling process. Diego also discusses the benefits of using dbt, Snowflake, and Azure DevOps in data modeling. There is also a parallel drawn between analytics and Diego’s connection with jazz music.

Speaker: Diego Morales, Civil Industrial Engineer, Watercare

Register for Coalesce at https://coalesce.getdbt.com

Gartner predicts over 40% of agentic AI projects will fail by 2027, despite AI transformation being a top industry priority. The cause is a classic "last-mile problem": AI agents require step-by-step instructions, but key workflows are undocumented. This session demonstrates how to create a roadmap for your AI transformation by developing living blueprints of your processes or architecture with tools like the Lucid Suite, Microsoft Teams, and Azure DevOps.