Filedot.to — Nn

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In this paper we ask:

The proliferation of deep‑learning frameworks has lowered the barrier to entry for neural‑network (NN) development, yet the workflow from data ingestion to model deployment remains fragmented. is a newly released cloud‑native service that unifies data storage, versioning, and compute resources under a single URL‑based interface. This paper presents a comprehensive analysis of Filedot.to’s architecture, its integration with popular deep‑learning libraries (TensorFlow, PyTorch, JAX), and a set of benchmark experiments that compare development speed, reproducibility, and inference latency against conventional pipelines based on separate storage (e.g., S3) and compute (e.g., VM, containers). Results indicate a 27 % reduction in end‑to‑end prototyping time and up to 15 % lower inference latency for models served directly from Filedot.to’s edge‑optimised inference layer. We conclude with best‑practice guidelines and open‑source tooling to leverage Filedot.to for both research and production workloads. filedot.to nn