Gradient Flow offers weekly analysis of applied AI and machine learning for enterprise teams, drawing on interviews with practitioners and recent incidents. Recent issues cover deep model customization, local versus hosted AI deployment, and security risks from AI agents.

Weekly essays on why AI initiatives at growth-stage startups fail for data governance and platform reasons rather than model reasons. Each issue uses concrete incidents, such as stale notebook pipelines, misleading catalogue descriptions and unmanaged documents, to explain the failure and what to change.

An engineering blog from Michelin's IT teams sharing first-hand write-ups on data architecture, developer tooling and AI adoption. Posts include concrete measurements, design reasoning and lessons learned from internal projects.

Tutorials and example queries for working with Google Analytics 4 and Google Ads data in BigQuery, including new SQL features and cost control. Free issues are supplemented by a premium archive with extras such as query accelerators.