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We answer these questions through a mixed‑methods evaluation: (i) a controlled user‑study measuring development time, (ii) micro‑benchmarks of inference latency across hardware tiers, and (iii) a qualitative analysis of version‑control semantics.

Deep learning research demands rapid iteration: data must be collected, pre‑processed, versioned, fed to a model, and the resulting artefacts (checkpoints, logs, visualisations) need to be stored and shared. Historically, practitioners stitch together disparate services—object stores (AWS S3, GCS), compute clusters (Kubernetes, SLURM), and experiment‑tracking tools (MLflow, Weights & Biases). This fragmentation introduces hidden latency, version‑control pain points, and reproducibility challenges. filedot.to nn

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