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Fully Connected keynote: Building tools for agents at Weights & Biases

Fully Connected keynote: Building tools for agents at Weights & Biases

A summary of the keynote by Lukas Biewald (Weights & Biases) and Camille Fournier (CoreWeave) at Fully Connected London 2025. They discuss recent product updates for W&B Models and Weave, the synergy behind the CoreWeave acquisition, and a deep dive into building and automating an autonomous software engineer agent.

Surviving the AI Workforce Shakeup

Surviving the AI Workforce Shakeup

Ben Lorica and Evangelos Simoudis analyze the nuances of AI-driven layoffs, categorizing them into upskilling gaps, automation, and strategic R&D shifts. They also explore the immense pressure for ROI on AI infrastructure investments, leading to the emergence of LLMOps as a form of financial management and the critical need for breaking down organizational silos.

Build and monitor multi-agent contact centers using Weights & Biases

Build and monitor multi-agent contact centers using Weights & Biases

This post explores the shift from costly legacy contact center software to multi-agent AI systems. It demonstrates how to build, monitor, and evaluate these complex agentic systems using the Weights & Biases AI Developer Platform, with a focus on tracing, quality assessment, and ensuring consistent customer support.

Production monitoring for AI applications using W&B Weave

Production monitoring for AI applications using W&B Weave

Learn how W&B Weave's online evaluations enable real-time monitoring of AI applications in production, allowing teams to track performance, catch failures, and iterate on quality over time using LLM-as-a-judge scores.

Trust at Scale: Security and Governance for Open Source Models // Hudson Buzby // MLOps Podcast #338

Trust at Scale: Security and Governance for Open Source Models // Hudson Buzby // MLOps Podcast #338

Hudson Buzby from JFrog discusses the critical security, governance, and legal challenges enterprises face when adopting open-source AI models. He highlights the risks lurking in repositories like Hugging Face and argues for a centralized, curated AI gateway as the essential framework for enabling safe, scalable, and cost-effective AI development.

Streamline evaluation, monitoring, optimization of AI data flywheel with NVIDIA and Weights & Biases

Streamline evaluation, monitoring, optimization of AI data flywheel with NVIDIA and Weights & Biases

A walkthrough of the NVIDIA Data Flywheel Blueprint, demonstrating how to use production data and Weights & Biases to systematically fine-tune AI agents. This process enhances model accuracy and efficiency by creating a continuous improvement cycle, moving beyond the limitations of prompt engineering.