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Model Metadata and Artifact Storage

Model files and release records have different storage needs. Immutable artifacts and a controlled release pointer help prevent partially uploaded models from reaching users.

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RELATED CONCEPTS

Dataset Lifecycle: Ingest, Shard and Retain Parallel Filesystems vs Object Storage Model Onboarding: From Hugging Face to Production Control Plane and API Design for GPU Platforms Evaluation and Data Pipeline Infrastructure

LESSONS THAT TEACH THIS

From Model to ServiceGive the service an identity and a reproducible home12 min→From Model to ServicePublish a model without mixing its versions22 min→From Model to ServiceChange the corpus without leaking old access12 min→

PRACTICE THIS IN REAL QUESTIONS

AI Infrastructure System DesignDesign an online and offline feature store without leaking future information into training→Networking, Interconnects & StorageParallel filesystem or object storage for training data? Make the call and explain the two-tier pattern most clusters end up with.→Distributed Training & ParallelismDesign a checkpoint format for thousands of GPUs: no gather on write, resumable at a different world size, no stall.→Napkin Math, Cost & CapacityEstimate how long it takes to write a checkpoint for a 405B training run→Napkin Math, Cost & CapacityWhat storage throughput does a 16k-GPU training cluster need?→Hardware, Cabling & Cluster Build-OutSize the storage for a 2,048-GPU training cluster. What numbers actually drive it?→