Introduction In this blog post we dive into inference with DBRX, the open state-of-the-art large language model (LLM) created by Databricks (see Introducing...

We released Ray support public preview last year and since then, hundreds of Databricks customers have been using it for variety of use...

In the previous blog , we discussed how to securely access Azure Data Services from Azure Databricks using Virtual Network Service Endpoints or...

Introduction Let’s explore a common scenario – your team is eager to leverage open source LLMs to build chatbots for customer support interactions...

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...