About 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.
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.
Voice
precise · code-first · math-friendly · production-minded
Sister sites
Priya Anand also writes for:
About This Publication
SentryML covers ML observability and MLOps from an engineering-first perspective — model monitoring, drift detection, training/serving skew, deployment reliability, and the operational discipline that keeps ML systems working in production.
ML engineers, platform teams, and data scientists responsible for keeping production ML systems reliable. Posts focus on implementation: what to instrument, how to interpret signals, and how to respond when models degrade.
What we cover
- Model monitoring architecture and tooling
- Drift detection: data, concept, and label drift
- Training/serving skew diagnosis and remediation
- MLOps deployment reliability and incident response
- Observability tooling comparisons and integration guides
Stay current
Subscribe to the RSS feed for new MLOps and observability posts. If you have a production monitoring architecture or post-mortem worth sharing, contact the editorial desk.