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Evaluation and Data Pipeline Infrastructure

Data pipelines prepare traceable training inputs; evaluation pipelines compare model behavior. Both need versioned inputs, reproducible execution and useful failure reporting.

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RELATED CONCEPTS

Data Loading Pipelines for Training Ray on Kubernetes Parallel Filesystems vs Object Storage Multi-Tenant Fine-Tuning Service Serving Engines: vLLM, SGLang and TensorRT-LLM Cost per Million Tokens

LESSONS THAT TEACH THIS

From Model to ServiceMeasure useful answers before renting more compute14 min→Inference EngineeringBudget reasoning before promising answer latency18 min→From Model to ServicePublish a model without mixing its versions22 min→

PRACTICE THIS IN REAL QUESTIONS

AI Infrastructure System DesignDesign the pipeline that produces 15 trillion training tokens: ingest, dedup, tokenize, shard, serve. Throughput per stage.→Distributed Training & ParallelismDesign a distillation pipeline that improves a target task without hiding capability regressions→AI Infrastructure System DesignDesign an asynchronous batch inference API for a billion records with a completion deadline→AI Infrastructure System DesignDesign the checkpoint store for a lab running several large training jobs: write bursts, retention, resharding and lineage.→AI Infrastructure System DesignDesign the eval pipeline for a frontier model: thousands of evals per checkpoint, sharded inference, caching, reproducible results.→AI Infrastructure System DesignDesign an online and offline feature store without leaking future information into training→