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
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About This Publication
ML Monitoring Report covers production ML monitoring — concept and label drift, training/serving skew, embedding-store reliability, online evaluation, and the instrumentation patterns that catch model degradation before it reaches users.
ML engineers and platform teams responsible for production model reliability. The focus is on practical monitoring architectures: what to instrument, how to alert, and what to do when a model starts drifting.
What we cover
- Drift detection: concept, label, and data drift
- Training/serving skew identification and remediation
- Embedding and vector store reliability monitoring
- Online evaluation and A/B testing for ML systems
- Alert design and incident response for model degradation
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