Observability

• Inside Vector Databases: Building Retrieval-Augmented Systems that Scale · 2025-10-26

  How modern vector databases ingest, index, and serve embeddings for production retrieval-augmented generation systems without falling over.

• Seeing in the Dark: Observability for Edge AI Fleets · 2024-08-16

  A practitioner's guide to instrumenting, monitoring, and debugging machine learning models running at the edge.

• Latency-Aware Edge Inference Platforms: Engineering Consistent AI Experiences · 2023-03-12

  A full-stack guide to designing, deploying, and operating low-latency edge inference systems that stay predictable under real-world constraints.

• Keeping the Model Awake: Building a Self-Healing ML Inference Platform · 2023-02-14

  A field report on taming production machine learning inference with proactive healing, adaptive scaling, and human empathy.

• Instrumenting Without Spying: Privacy-Preserving Telemetry at Scale · 2021-05-27

  How we rebuilt our telemetry pipeline to respect user privacy without sacrificing insight.

• Safe Rollback Strategies for Distributed Databases · 2020-11-08

  A comprehensive guide to designing, executing, and validating rollbacks in distributed database environments without compromising data integrity or customer trust.