How to Build Production-Ready Apps in Replit with Lakebase Postgres | Databricks + Replit Demo

Diagnose Manufacturing OEE Issues with Genie Agents

Databricks Genie allows manufacturing teams to diagnose equipment issues through natural language questions, enabling a production manager to identify a faulty welding station and quantify its impact within five minutes without writing SQL. The demo shows how data already siloed across multiple systems becomes immediately actionable when accessed conversationally.

Databricks for Good x Toast91: Redefining how Young People access Mental Health Support

Toast91 provides conversational AI to help young people access mental health support in a more natural and accessible way, especially at times when traditional services are unavailable. Databricks partnered with them to address safety, ethics, data privacy, and content accuracy, helping establish frameworks for ensuring AI-powered mental health services deliver correct and unbiased support.

Databricks for Good x Global Orphan (GO) Project: Turning Insight into Impact

GO Project tackles the orphan cycle through three initiatives—Care Portal, Strong Families, and fair trade apparel—but struggled to effectively leverage their data until implementing Databricks. Databricks' AI tools like Genie code enabled their three-person team to perform advanced analytics without deep SQL expertise, allowing them to refocus on their core mission of helping vulnerable families.

How to Build and Serve Production ML Features | Databricks Feature Store Demo

Databricks Feature Store provides a unified feature views abstraction for batch and streaming data with automatic point-in-time joins for training and built-in MLflow experiment tracking. The same feature definitions deploy directly to production with online serving on Lake Base, eliminating the need to rewrite features while enabling model and feature serving to scale together.

How to Build and Serve Production ML Features | Databricks Feature Store Demo

3. What is Unity AI Gateway | How to Control AI Spending

Unity AI Gateway sits in front of multiple AI models and lets you abstract them behind a single endpoint, enabling seamless model swaps without changing application code. Every request gets logged to system tables, giving you centralized visibility into spending and usage per model, user, and tool.