01
Evaluation, retrieval, and serving paths
How model-backed products depend on data quality, feedback loops, and operational discipline.
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AI · Data · BigData
Field notes for AI, data, and BigData builders.
Exploring the systems, patterns, and tradeoffs behind production-grade data and AI platforms. From pipelines to models, from streaming to distributed jobs, these are the notes I wish I had when I was in the trenches.
Writing from the machine room.
Deep dives, patterns, and real-world lessons from systems that move data, run models, and hold up under load.
02
Trustworthy movement and shape
Streams, lakes, transformations, contracts, lineage, and the ergonomics that make data usable.
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Distributed lessons that still matter
EMR, Spark, Kinesis, DynamoDB, Elasticsearch, and the reliability habits around them.
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Recent writing at the practical overlap of AI, distributed processing, and operational clarity.
View all 35 notes →From EMR Escalations to AI Platform Reliability
A practical bridge from big data escalation work to AI platform reliability: the same habits show up in scheduling, observability, data quality, and recovery.
The GPU Cluster Has a Data Problem First
The expensive part of an AI platform is not only the accelerator. It is the end-to-end path that keeps training and inference workloads fed, observable, and recoverable.
Follow the threads.
Curated paths through connected ideas and recurring topics.