Today, we are excited to announce the general availability of Feature Serving. Features play a pivotal role in AI Applications, typically requiring considerable...
Background In an era where Retrieval-Augmented Generation (RAG) is revolutionizing the way we interact with AI-driven applications, ensuring the efficiency and effectiveness of...
Introduction Databricks Lakehouse Monitoring allows you to monitor all your data pipelines – from data to features to ML models – without additional...
Retrieval Augmented Generation (RAG) is an efficient mechanism to provide relevant data as context in Gen AI applications. Most RAG applications typically use...
Retrieval-Augmented-Generation (RAG) has quickly emerged as a powerful way to incorporate proprietary, real-time data into Large Language Model (LLM) applications. Today we are...
We recently announced our AI-generated documentation feature , which uses large language models (LLMs) to automatically generate documentation for tables and columns in...
Today we're excited to announce MLflow 2.8 supports our LLM-as-a-judge metrics which can help save time and costs while providing an approximation of...
Last year, we published the Big Book of MLOps, outlining guiding principles, design considerations, and reference architectures for Machine Learning Operations (MLOps). Since...