Researchers at the company looked into how malicious fine-tuning makes a model go rogue, and how to turn it back.

Databricks Agent Bricks automates enterprise AI agent optimization and evaluation, eliminating manual processes that block production deployments.

Agent Bricks uses Mosaic AI's advanced methods to create domain-specific synthetic data and task-focused benchmarks.

Mosaic AI Model Training enables fine-tuning for foundation models, increasing model quality and decreasing cost Mosaic AI Model Training fine-tunes open source foundation models with an ...

For this, Databricks now offers the Mosaic AI Model Training service, which — you guessed it — allows its users to fine-tune models with their organization’s private data to help them ...

Learn more AI models perform only as well as the data used to train or fine-tune them. Labeled data has ... developers create A I apps rapidly. The Mosaic research team at Databricks developed ...

This article explores four key methods—prompting LLMs, building retrieval-augmented generation (RAG) systems, fine-tuning LLMs and developing AI agents—and evaluates their role in shaping the ...

Fine-tuning is a process that refines a pre-trained AI model, allowing it to produce more tailored outputs with just a small amount of additional training. Unlike the initial bulk training ...

Vertical AI’s upcoming platform No-Code Studio will empower users from all backgrounds to fine-tune and integrate AI models. Artificial intelligence has not taken over the world —at least yet ...

Fine-tuning refers to a process of training a pre-trained AI model on a curated dataset to create a smaller new model, which is then capable of producing more specific kinds of outputs.