Matei Zaharia
@matei-zaharia.bsky.social
CTO at Databricks and CS professor at UC Berkeley. https://people.eecs.berkeley.edu/~matei/
This is a joint effort across our engineering and research teams, based on new tuning methods we developed like TAO and ALHF. I think this type of declarative development is the future of AI -- help users build evals and auto-optimize based on those. Try it today!
June 11, 2025 at 5:08 PM
This is a joint effort across our engineering and research teams, based on new tuning methods we developed like TAO and ALHF. I think this type of declarative development is the future of AI -- help users build evals and auto-optimize based on those. Try it today!
Moreover, to steer your agents in Agent Bricks, you can use natural language feedback; the system optimizes all components of the agent (e.g. retrievers, guardrails, etc) based on it -- something we call Agent Learning from Human Feedback (ALHF). More feedback = better agent.
June 11, 2025 at 5:08 PM
Moreover, to steer your agents in Agent Bricks, you can use natural language feedback; the system optimizes all components of the agent (e.g. retrievers, guardrails, etc) based on it -- something we call Agent Learning from Human Feedback (ALHF). More feedback = better agent.
Agent Bricks automatically searches and combines the latest AI development techniques to give you a high-quality. It gets really great results quickly compared to DIY agents, e.g. state of the art performance on information extraction and question answering out of the box.
June 11, 2025 at 5:08 PM
Agent Bricks automatically searches and combines the latest AI development techniques to give you a high-quality. It gets really great results quickly compared to DIY agents, e.g. state of the art performance on information extraction and question answering out of the box.
Congrats Justine!
May 31, 2025 at 8:54 PM
Congrats Justine!
Key to TAO is a search and scoring process that leverages test-time compute only during training, and new RL methods and models from our team. More details, in our blog: www.databricks.com/blog/tao-usi...
March 25, 2025 at 5:47 PM
Key to TAO is a search and scoring process that leverages test-time compute only during training, and new RL methods and models from our team. More details, in our blog: www.databricks.com/blog/tao-usi...
TAO's trained model quality also scales with compute spent during training, not with human labeling effort, and the resulting models are always low inference cost.
March 25, 2025 at 5:47 PM
TAO's trained model quality also scales with compute spent during training, not with human labeling effort, and the resulting models are always low inference cost.
Our new method, Test-time Adaptive Optimization (TAO), only needs input examples of a task and can outperform supervised fine-tuning on thousands of human-labeled examples. It brings efficient OSS models like Llama to the quality of expensive larger models.
March 25, 2025 at 5:47 PM
Our new method, Test-time Adaptive Optimization (TAO), only needs input examples of a task and can outperform supervised fine-tuning on thousands of human-labeled examples. It brings efficient OSS models like Llama to the quality of expensive larger models.