Francisco Booth conducted eight structured research sessions with ML engineers running fine-tuning workloads on rented GPU providers (RunPod, Vast.ai, Lambda Labs) to identify common failure patterns. The most expensive failures were not crashes but silent completions where jobs exited successfully while producing useless models. Three primary failure categories emerged: checkpoint loss due to writing weights to ephemeral container storage (e.g., ./output, ~/.cache) instead of persistent volumes like /workspace, resulting in total data loss upon pod termination; silent garbage outputs caused by data issues like duplicate rows poisoning LoRA updates, conflicting labels, or class weight misconfiguration collapsing binary cross-entropy loss to ln(2) (0.693) while GPU utilization remained high; and a universal absence of error messages for these scenarios. One enterprise team maintained a five-to-six-part manual bash script for pre-flight environment, storage, and dependency checks because no standardized tool existed. This research shifted ComputeFence's focus from CUDA/CPU fallback detection (24s vs 0.4s iteration) to storage validation as the primary check. The tool's 30-second pre-flight (uvx computefence doctor) prioritizes: HuggingFace cache path validation (HF_HOME), checkpoint directory ephemeral prefix detection, disk headroom warnings (<20GB) and blocks (<5GB), GPU/CUDA visibility via torch.cuda.is_available(), Accelerate config/GPU count mismatches, and optional dataset integrity scans for duplicates and label conflicts. Each warning outputs the exact remediation command. The tool targets preventable configuration errors visible before training starts, acknowledging it cannot guarantee model quality or convergence.