PySpark has always provided wonderful SQL and Python APIs for querying data. As of Databricks Runtime 12.1 and Apache Spark 3.4, parameterized queries...
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...
Following the announcements we made last week about Retrieval Augmented Generation (RAG) , we're excited to announce major updates to Model Serving. Databricks...
Retrieval Augmented Generation (RAG) is an efficient mechanism to provide relevant data as context in Gen AI applications. Most RAG applications typically use...
Following the announcement we made yesterday around Retrieval Augmented Generation (RAG) , today, we’re excited to announce the public preview of Databricks Vector...
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...
Managing the environment of an application in a distributed computing environment can be challenging. Ensuring that all nodes have the necessary environment to...