Summary
MLflow Model Serving offers one-click deployment of models for real-time inference, with deployed versions managed through the MLflow Model Registry. The demo covers deploying and validating different model versions, consuming a served model from Power BI to build a prediction report, and managing access rights and monitoring.
Summary from the captured archive. Follow the original source for full context.
More from Databricks
NewsCreating a Digital Twin with Spark Real-Time Mode and Lakebase
This video demonstrates how to create a real-time digital twin for a manufacturing cell using Databricks tools such as Spark Real-Time Mode, Lakebase, Databricks Apps, and Unity Catalog. It explains how processing sensor readings instantly allows operators to detect and act on equipment faults, such as a hot spindle, before they negatively impact production quality and efficiency.
NewsRespond to Airport Disruptions Using Lakehouse//RT
Lakehouse//RT and Zerobus enable low latency query processing to help airline operations quickly manage flight disruptions and rebook passengers. This technology allows multiple teams to access real-time data simultaneously without creating a duplicate system, significantly improving decision-making speed during emergencies.
TutorialsLakebase Search: Build AI Retrieval for Agents with Lakebase Postgres
Lakebase Postgres introduces lakebase vector and lakebase text extensions to support hybrid search for AI agents. It leverages the Databricks Lakehouse architecture to synchronize and index data for efficient vector and full text search without requiring external databases.
TutorialsGoverning AI Agents with Unity Catalog and Unity Gateway on Databricks | Unity Catalog Demo
The video demonstrates how to govern AI agents using Unity Catalog and Unity Gateway on Databricks. It shows setting up access policies, user defined functions, and role filters to control data and model interactions.
TutorialsDatabricks Pipelines: Write to One Unity Catalog Table from Multiple Sources with FLOWs
Databricks pipelines now allow writing to a managed Unity Catalog table from multiple sources by using one flow per source. Users can migrate existing structured streaming jobs without reprocessing the source data by utilizing checkpoint import features.
