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...
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Announcing MLflow 2.8 LLM-as-a-judge metrics and Best Practices for LLM Evaluation of RAG Applications, Part 2
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...
Simplifying Production MLOps with Lakehouse AI
Machine learning (ML) is more than just developing models; it's about bringing them to life in real-world, production systems. But transitioning from prototype...
Llama 2 Foundation Models Available in Databricks Lakehouse AI
We’re excited to announce that Meta AI’s Llama 2 foundation chat models are available in the Databricks Marketplace for you to fine-tune and...
Announcing Inference Tables: Simplified Monitoring and Diagnostics for AI models
Have you ever deployed an AI model, only to discover it's delivering unexpected results in a real-world setting? Monitoring models is as crucial...
Deploy Private LLMs using Databricks Model Serving
We are excited to announce public preview of GPU and LLM optimization support for Databricks Model Serving! With this launch, you can deploy...
Introducing MLflow 2.7 with new LLMOps capabilities
As part of MLflow 2’s support for LLMOps, we are excited to introduce the latest updates to support prompt engineering in MLflow 2.7...
Best Practices for LLM Evaluation of RAG Applications
Chatbots are the most widely adopted use case for leveraging the powerful chat and reasoning capabilities of large language models (LLM). The retrieval...
Using MLflow AI Gateway and Llama 2 to Build Generative AI Apps
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...
What’s new with Unity Catalog at Data and AI Summit 2023
The fundamental principles of governance – accountability, compliance, quality, and transparency – that are essential for data management have now become equally imperative...
Lakehouse AI: A Data-Centric Approach to Building Generative AI Applications
Generative AI will have a transformative impact on every business. Databricks has been pioneering AI innovations for a decade, actively collaborating with thousands...
Announcing MLflow 2.4: LLMOps Tools for Robust Model Evaluation
LLMs present a massive opportunity for organizations of all scales to quickly build powerful applications and deliver business value. Where data scientists used...
Announcing General Availability of Databricks Model Serving
ML Virtual Event Enabling Production ML at Scale With Lakehouse March 14, 9 AM PDT / 4 PM GMT Register Now We are...
Recap of Databricks Machine Learning announcements from Data & AI Summit
Databricks Machine Learning on the lakehouse provides end-to-end machine learning capabilities from data ingestion and training to deployment and monitoring, all in one...
Supercharge Your Machine Learning Projects With Databricks AutoML — Now Generally Available!
Machine Learning (ML) is at the heart of innovation across industries, creating new opportunities to add value and reduce cost. At the same...
Announcing Databricks Autologging for Automated ML Experiment Tracking
Machine learning teams require the ability to reproduce and explain their results--whether for regulatory, debugging or other purposes. This means every production model...
Introducing Databricks AutoML: A Glass Box Approach to Automating Machine Learning Development
Today, we announced Databricks AutoML , a tool that empowers data teams to quickly build and deploy machine learning models by automating the...
Introducing the Databricks Web Terminal
Introduction We're excited to introduce the public preview of the Databricks Web Terminal in the 3.25 platform release. Any user with "Can Attach...