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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
Systems that move, learn, and scale.
Field notes from the machine room. Patterns, tradeoffs, and operational lessons from building production-grade data and AI platforms.
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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03
Distributed lessons that still matter
EMR, Spark, Kinesis, DynamoDB, Elasticsearch, and the reliability habits around them.
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Latest notes.
Recent writing at the practical overlap of AI, distributed processing, and operational clarity.
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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.
Observability Before Orchestration
Orchestration helps only after the system can explain itself. Metrics, traces, logs, and workload shape need to come before automation confidence.
SLO Thinking for Data Pipelines That Feed AI Systems
Data pipelines need reliability language too. Freshness, completeness, latency, correctness, and recoverability are better signals than green checkmarks.
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.
