Learn How to Reliably Monitor Your Data and Model Quality in the Lakehouse
Description
Developing and upkeep of production data engineering and machine learning pipelines is a challenging process for many data teams. Even more challenging is monitoring the quality of your data and models once they go into production. Building upon untrustworthy data can cause many complications for data teams. Without a monitoring service, it is challenging to proactively discover when your ML models degrade over time, and the root causes behind it. Furthermore, with a lack of lineage tracking, it is even more painful to debug errors in your models and data. Databricks Lakehouse Monitoring offers a unified service to monitor the quality of all your data and ML assets. In this session, you’ll learn how to: - Use one unified tool to monitor the quality of any data product: data or AI - Quickly diagnose errors in your data products with root cause analysis - Set up a monitor with low friction, requiring only a button click or a single API call to start and automatically generate out-of-the-box metrics - Enable self-serve experiences for data analysts by providing reliability status for every data asset Talk by: Kasey Uhlenhuth and Alkis Polyzotis Connect with us: Website: https://d…
Description from YouTube. Full content on the video page.
Topics
More from Databricks
NewsParallel Coding Agents with Lakebase | Claude Code + GitHub Actions
This video demonstrates how to run multiple coding agents in parallel by combining git worktrees, GitHub Actions, and Lakebase database branching. It shows how each agent automatically receives an isolated database branch for safe experimentation and schema migrations, followed by dedicated preview environments for pull requests.
EventsHow Enterprises Govern AI Agents Across Multiple Models
Databricks announced the general availability of the Unity AI gateway to provide centralized multi-model governance, cost controls, and end-to-end observability for enterprise AI agents. Panelists discussed how coding agents and harnesses are evolving beyond programming into long-running operations, personal software development, and automated organizational workflows.
EventsDemo: Building a Governed AI Agent with Unity AI Gateway
This video demonstrates how to build, update, and govern a store operations AI agent using Databricks Agent Bricks and the Unity AI Gateway. The tutorial highlights integrating custom Model Context Protocol servers, recording execution traces with MLflow, and enforcing security policies and budget controls.
NewsHow ModMed Transforms Healthcare AI and Agentic Workflows with Databricks
ModMed uses the Databricks Lakehouse platform and Unity Catalog to build secure AI-enabled healthcare applications and agentic workflows. The integration of these tools allows both technical and non-technical users to access near real-time data insights and solve complex problems efficiently.
NewsTeach AI how your business actually runs
Model intelligence is no longer the bottleneck for enterprise AI adoption because modern frontier models easily handle complex reasoning tasks. Business value requires providing these models with specific organizational context and metadata about internal processes to create a competitive advantage.