A research note on making distributed AI services observable, recoverable, and easy to reason about.
CloudAIDistributedSystemsReliability
CLOUD AI RESEARCH
Academic Minimalism
Research notes on cloud-native AI systems, distributed training, and infrastructure.
Writing by year
No thumbnails. Just text.A note on why training jobs, schedulers, and observability should be designed together.
CloudInfraDistributedTrainingObservability
How I annotate OSDI papers to recover the design constraints hidden between the lines.
PaperReviewSystemsResearchWorkflow
A short note on treating observability as a first-class training primitive rather than a postmortem tool.
DistributedTrainingObservabilityMLOps
A lightweight framework for deciding when to retrain versus when to recalibrate.
EdgeAIEvaluationMLOps
A note on why accuracy alone is not enough when inference has to live on constrained devices and unstable networks.
EdgeAIInferenceSystemsDesign