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All digests tagged Weights & Biases (W&B)
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Learn how to build reproducible AI agent evaluation pipelines using WolfBench. Discover why single scores are insufficient and how to track consistency, cost, and capability across six critical pipeline layers.
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Pinterest details its massive ML platform migration from MLflow to Weights & Biases. Learn about achieving system parity, zero downtime, and implementing governance guardrails at scale.
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ARIA, a self-improving AI agent, uses a closed-loop framework to take production traces, convert them into offline eval tasks, find bugs, write fixes, and benchmark the new version.
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CoreWeave ARIA is an AI research agent built into Weights & Biases that accelerates the self-improving AI loop. It automates auto-research, analyzes training runs, and optimizes agent performance.
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Learn how to add full observability tracing to an AI agent using W&B Weave. This process uses the W&B MCP server and SDK, allowing deep inspection of conversations, turns, LLM calls, and tool usage without modifying existing code.
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Speakers at the AI Engineer World's Fair debated the shift from centralized cloud LLMs to local, open-source models. Key topics included model routing (Fugu), agentic workflows using W&B's Coreweave Ara, and ensuring data sovereignty through on-device inference.
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