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Beyond demos, notebooks and hype

Showing posts for Observability. Longer essays and technical guides on MLOps, data engineering, AI architecture, Azure and the platform concerns that tend to show up after the model finally works.

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Hand-drawn sketch of a green success response hiding retry loops, failed backend services and mock data

Architectural guide July 20, 2026 · 12 min read

AI-generated code risks: when fallbacks and mocks hide production failures

AI-generated code risks are often not obvious syntax errors. The harder problem is code that catches real failures, retries them badly, returns mock data and still reports success.

ai-architectureproduction-ml

Hand-drawn observability sketch showing dashboard panels for service health, data quality, prediction behavior and business feedback

Production checklist June 7, 2026 · 5 min read

Minimal viable ML Observability: what to monitor first

A practical starting point for ML observability: what to monitor first, which signals matter early, and how to avoid building a monitoring stack that is bigger than the model itself.

mlopsobservability