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.

Westat, a leader in data-driven research for 60 years+, has implemented a centralized Databricks platform to support hundreds of research projects for government, foundations, and private clients. This initiative modernizes Westat’s technical infrastructure while maintaining rigorous statistical standards and streamlining data science. The platform enables isolated project environments with strict data boundaries, centralized oversight, and regulatory compliance. It allows project-specific customization of compute and analytics, and delivers scalable computing for complex analyses. Key features include config-driven Infrastructure as Code (IaC) with Terragrunt, custom tagging and AWS cost integration for ROI tracking, budget policies with alerts for proactive cost management, and a centralized dashboard with row-level security for self-service cost analytics. This unified approach provides full financial visibility and governance while empowering data teams to deliver value. Audio for this session is delivered in the conference mobile app, you must bring your own headphones to listen.

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.