Databricks releases Adaptive Instructed-Retriever for faster search
The model combines parallel and sequential retrieval to match third-party quality at double the speed, optimizing cost through online reinforcement learning.
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In short: Databricks raised $5 billion at a $190 billion valuation, reaching a $7 billion revenue run-rate and launching new agentic AI solutions.
The model combines parallel and sequential retrieval to match third-party quality at double the speed, optimizing cost through online reinforcement learning.
Co-founder Reynold Xin delivers the opening keynote on agentic database paradigms while engineers present four technical papers on streaming architecture, query optimization, and view maintenance frameworks.
The architecture decouples compute and storage, enabling instant branching, point-in-time recovery, and scale-to-zero capabilities for AI workloads.
Funding led by Coatue, with $190B valuation and >80% YoY growth; Lakebase revenue run-rate exceeds $100M.
Databricks, a leading data analytics and AI software company, is set to invest over $1 billion in...
Artificial intelligence firm Databricks and a group of authors suing for copyright infringement are trading arguments over why a California federal court should rule their...
The data and AI company is one of the world’s strongest private companies, but a PitchBook report says it’s unattractive at the current price.
The US Department of Justice is examining whether the venture capital firm violated antitrust laws by representing two direct competitors on its boards.
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