Mosaic AI
Recent items mentioning Mosaic AI across the Databricks ecosystem — releases, news, videos, and community Q&A. Updated hourly.
Databricks Unity AI Gateway introduced Smart Routing to automatically direct coding tasks across models and harnesses, achieving frontier-level quality at more than 30% lower cost per task 10. For governance and observability, administrators can now restrict models and model services using catalog tags paired with attribute-based access control (ABAC) grant policies 1, monitor LLM usage for data leaks via traces 8, and enforce spending budgets across users, groups, and workspaces 9.
Generated daily from the 10 most recent items mentioning Mosaic AI. Click any [N] to jump to the source.
MLflow 3.15.0 introduces an MCP Registry for registering and sharing Model Context Protocol servers, enhances the Assistant with multi-provider LLM support and per-session token usage tracking, and enables proxy-less artifact transfers via presigned URLs to reduce server load and timeouts on large files. Additional improvements include sharable Runs table views, multi-modal image attachments for LLM judges to evaluate vision tasks, and numerous bug fixes across tracing, evaluation, gateway, and UI components.
Building an Enterprise RAG Chatbot with Databricks Mosaic AI and Vector Search
How ERGO Hestia reduced time-to-market with Lakebase and Mosaic AI Model Serving
ERGO Hestia modernized its real-time pricing engine with Databricks Lakebase and Mosaic AI Model Serving, reducing time-to-market by unifying data, features, and decisions for millisecond pricing. This eliminated extraction overhead and fragmented governance from their previous multi-hop architecture, enabling faster model deployment and instant market response.
Azure OpenAI v1 API support for External Model Serving / Mosaic AI Gateway?
Building a Real-Time Field Sales App on Databricks with Lakeflow, Lakebase, and Mosaic AI
Bring Databricks into Kiro IDE with the AI Dev Kit Power
The Databricks AI Dev Kit Power now offers a one-click setup to integrate Kiro IDE with the full Databricks platform, providing AI-assisted development grounded in your workspace's Unity Catalog metadata. This new path, alongside a lighter PAT-based option, ensures your AI assistant writes SQL with actual columns and respects all row, column, and tag-based grants.
I built a 54-minute hands-on RAG tutorial on Databricks — from PDF loading to retrieval and LLM answers
Hi Everyone I recently published a hands-on tutorial where I build a basic **RAG pipeline on Databricks** from scratch. The goal of the video is not just to use a high-level RAG framework, but to show what actually happens behind the scenes. In the video, I cover: * Loading PDF files inside Databricks * Extracting text from PDF pages * Splitting documents into chunks * Creating embeddings using Databricks embedding endpoints * Building a simple manual retrieval system using vector similarity * Creating prompts from retrieved chunks * Generating grounded answers using Databricks LLM endpoints * Using `databricks-langchain` for embeddings and chat models I intentionally kept the implementation simple so that beginners can understand the core mechanics of RAG before moving to more production-level tools like Vector Search, Unity Catalog, MLflow, etc. Here is the video: [https://youtu.be/7QY1iXPLgRg](https://youtu.be/7QY1iXPLgRg) Would love to hear feedback from people working with Databricks, RAG, LangChain, or enterprise GenAI systems. Also curious: for production RAG on Databricks, would you prefer starting with a simple manual implementation like this first, or directly using Mosaic AI Vector Search / Databricks Vector Search from the beginning?
TutorialsDatabricks End-To-End Project | Zero-To-Expert | Streaming, AI, Lakeflow, Unity Catalog, AI/BI
This video demonstrates building an end-to-end restaurant analytics platform on Databricks, covering streaming and batch data ingestion, AI-powered sentiment analysis, and dashboard creation. It teaches how to use Unity Catalog, Lake Flow Connect for CDC, Spark declarative pipelines for real-time data from Event Hub, and how to construct a medallion architecture with fact and dimension tables.
NewsGetting GenAI to Production with Mosaic AI Gateway in Databricks
The video demonstrates how to productionize GenAI applications using Databricks' Mosaic AI Gateway, highlighting features like usage tracking, inference tables, AI guardrails, rate limits, and model fallbacks. It shows how to configure these features through the Databricks UI and monitor application performance and costs using built-in dashboards.
NewsAI Agents for Marketing: Leveraging Mosaic AI to Create a Multi-Purpose Agentic Marketing Assistant
7-Eleven built an internal AI marketing assistant using Mosaic AI and LangGraph that coordinates multiple specialized agents to generate campaign concepts, copy, and multichannel content for their marketing teams. The system uses a supervisor agent to orchestrate a creative campaign generator and copywriter, includes tools like web search and email, incorporates human-in-the-loop approval steps, and demonstrates measurable time savings.
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