Databricks adds new migration Brickbuilder Solutions to help customers succeed with AI
For the past two years, Databricks has collaborated with leading consulting partners to build innovative solutions for industry, migration, and data and AI...
Announcing Ray Autoscaling support on Databricks and Apache Spark™
Ray is an open-source unified compute framework that simplifies scaling AI and Python workloads in a distributed environment. Since we introduced support for...
Parameterized queries with PySpark
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...
Introducing Mixtral 8x7B with Databricks Model Serving
Today, Databricks is excited to announce support for Mixtral 8x7B in Model Serving . Mixtral 8x7B is a sparse Mixture of Experts (MoE)...
Offline LLM Evaluation: Step-by-Step GenAI Application Assessment on Databricks
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...
Lakehouse Monitoring: A Unified Solution for Quality of Data and AI
Introduction Databricks Lakehouse Monitoring allows you to monitor all your data pipelines – from data to features to ML models – without additional...
Build GenAI Apps Faster with New Foundation Model Capabilities
Following the announcements we made last week about Retrieval Augmented Generation (RAG) , we're excited to announce major updates to Model Serving. Databricks...
Improve your RAG application response quality with real-time structured data
Retrieval Augmented Generation (RAG) is an efficient mechanism to provide relevant data as context in Gen AI applications. Most RAG applications typically use...
Introducing Databricks Vector Search Public Preview
Following the announcement we made yesterday around Retrieval Augmented Generation (RAG) , today, we’re excited to announce the public preview of Databricks Vector...
Creating High Quality RAG Applications with Databricks
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...