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
MLOps Platforms publishes comparison-grade reviews of MLOps infrastructure — feature stores, model registries, training orchestration, online inference platforms, and experiment tracking — with honest assessments of where each platform excels and where it falls short.
ML engineers, platform teams, and technical leads evaluating MLOps tooling for their organization. Reviews cover real integration experience, scaling behavior, operational overhead, and total cost of ownership.
What we cover
- MLOps platform reviews: feature stores, registries, orchestration
- Head-to-head comparisons for common use cases
- Scaling behavior and operational overhead analysis
- Total cost of ownership and build-vs-buy assessment
- Integration complexity with major cloud providers
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