Operational depth. No editorial filler.
Articles focused on production decisions: pipeline failure modes, ML deployment trade-offs, agentic AI architecture, and attribution modeling that ties spend to revenue.
Implementation over opinion
Why your ingestion layer breaks at scale
Serving a model is not deploying a model
Designing agent loops that don't spiral
The three architectural decisions that cause batch pipelines to fail silently in production — and the idempotency patterns that prevent them.
Feature drift, latency budgets, and shadow-mode testing: what a production ML system actually requires beyond a containerized endpoint.
Tool-call orchestration, interrupt handling, and the guardrail architecture that keeps autonomous agents inside defined operational boundaries.
Multi-touch attribution without the mythology
Lakehouse vs. warehouse: the production trade-offs
Churn models that actually reduce churn
A prediction score is overhead unless it triggers an automated intervention. The feedback loop architecture that closes the gap between model output and revenue impact.
Data-driven attribution requires more than a last-click swap. Here is what the model actually needs and where the training data usually breaks.
Choosing a storage layer is a latency and cost decision, not a vendor preference. The criteria that actually matter once workloads hit real volume.