A distributed training job stops when any one worker fails, and what it loses depends on its checkpoints. This guide covers the job lifecycle, distributed failure modes, what a checkpoint must contain, atomic writes, verified resumption, and choosing the interval with a waste model.
A GPU cluster can show free devices and still be unable to start a job. This guide covers GPU memory and compute, placement and fragmentation, gang scheduling, quotas with borrowing, sharing modes and their isolation limits, and capacity under failure, with local simulations.
A model version number says little about what the model was built from or what it needs to run. This guide builds a small lineage and registry to show which artifacts a change invalidates, what reproducibility requires, and how promotion and rollback depend on compatibility.
A shared AI platform works only when it is clear what the platform promises and what each consuming team still owns. This guide covers consumer types, contracts, ownership, tenancy, and the runtime and evaluation services, with a runnable contract checker.