Author
Priya Anand
ML engineer turned MLOps, ex-FAANG. Builds and breaks AI pipelines at scale. Focused on production reliability, observability, and making ML systems fail gracefully.
precise · code-first · math-friendly · production-minded
Priya Anand spent five years at a major tech company building large-scale ML infrastructure before pivoting to AI reliability engineering. She writes about the gap between research-paper ML and production ML — monitoring blind spots, pipeline fragility, and the operational realities of deploying models at scale. Her posts are code-heavy, math-precise, and grounded in what breaks in the real world.
Also writes for
Posts (2)
- ops
Silent Quality Decay in Production LLM Apps: How to Detect Drift Before Users Do
Your eval scores are green. Customer complaints are up. The gap between offline metrics and production reality is the biggest reliability problem in LLM ops — here's how to close it.
- site
What this site is for
ML Monitoring Report covers ML observability and MLOps from a production-engineering perspective. Here's what we publish.