The Big Book of Generative AI: Best practices for building production-quality GenAI applications. Learn to build, deploy, and fine-tune models with code samples and expert insights.| Databricks
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To build customer support bots, internal knowledge graphs, or Q&A systems, customers often use Retrieval Augmented Generation (RAG) applications which leverage pre-trained models together with their proprietary data. However, the lack of guardrails for secure credential management and abuse prevention prohibits customers from democratizing access and development of these applications. We recently announced the MLflow AI Gateway, a highly scalable, enterprise-grade API gateway that enables org...| Databricks
Enhance RAG and generative AI applications with Databricks Vector Search, offering fast, secure similarity search over unstructured documents.| Databricks
Enhance RAG applications with real-time structured data using Databricks' Feature and Function Serving, simplifying Gen AI application deployment.| Databricks
Discover how to build high-quality Retrieval-Augmented Generation (RAG) applications using Databricks.| Databricks
Speed up GenAI app development with Databricks' new foundation model capabilities, offering best practices for production-quality applications.| Databricks
This blog post discusses best practices for evaluating retrieval-augmented generation (RAG) applications using large language models (LLMs).| Databricks
JetBlue is actively using artificial intelligence and machine learning across its business and actively using generative AI for its internal operations and ultimately revenue-producing products. Speaking at the Databricks Data + AI Summit, Sai Ravuru, Senior Manager of Data Science and Analytics at the airline, walked through how the company was using Databricks Lakehouse on multiple fronts. Databricks launched a series of new additions to its platform and said it will acquire MosaicML.| Constellation Research Inc.
Storage Format | mlflow.org