Evaluation, retrieval, and serving paths
How model-backed products depend on data quality, feedback loops, and operational discipline.
Built at Scale
Field notes for AI, data, and BigData builders.
A technical journal on how data moves, where distributed systems bend, and what it takes to turn AI-adjacent ideas into reliable production paths.
AI systems Data platforms Streaming BigData
Writing from the machine room.
This site is for practical architecture notes: the parts of AI and data systems that do not fit neatly into diagrams, but decide whether a platform is useful under load.
Trustworthy movement and shape
Streams, lakes, transformations, contracts, lineage, and the ergonomics that make data usable.
Distributed lessons that still matter
EMR, Spark, Kinesis, DynamoDB, Elasticsearch, and the reliability habits around them.
Latest notes.
Recent writing follows the practical overlap of AI systems, distributed processing, platform reliability, and clear operational tradeoffs.
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.
When Spark Fails Quietly Before the Logs Agree
Some Spark incidents do not begin with a clean error. They begin as shape changes: skew, executor churn, storage pressure, and queues that stop draining.
EMR Escalation Playbooks and Enterprise Blast Radius
A senior escalation playbook should reduce uncertainty: who is affected, which layer is failing, what changed, and what the safest next move is.
Start with a thread.
Choose a lane and follow the notes across incidents, design choices, tradeoffs, and the quiet engineering work behind dependable systems.