Ines Marlow

An independent publication about the engineering behind fashion AI

AIFashion.tech is a one-person editorial project run by Ines Marlow. Ines is a machine-learning engineer who moved into technical writing to cover the gap between vendor marketing and the actual architecture decisions practitioners face.

The site publishes deep-tech explainers on generative design, computer vision, model architecture, dataset construction and the infrastructure patterns — including tenant isolation and self-learning systems — that make production AI in fashion work. All articles are researched from publicly available sources: academic papers, open-source repositories, technical documentation and on-record writing. No anonymous briefings. No sponsored content. No affiliate arrangements.

The publication is independent. It has no funding relationship, editorial partnership or commercial agreement with any company in the fashion or technology industries.

If you find a factual or technical error, please use the contact page. Confirmed corrections appear inline in the article with a visible timestamp and a plain-language description of what changed.

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Editorial mission

Architecture first

Every article starts from how the system is built, not from what a vendor claims it does. Trade-offs, failure modes and design constraints are treated as the main subject, not as footnotes.

Public sources only

Claims are traceable to published papers, open repositories or on-record documentation. If something cannot be sourced publicly, it does not appear as a stated fact.

Corrections are part of the record

Technical writing has a longer shelf life than news. When an article contains an error, the correction is made visible in the text with a timestamp so readers who saved or linked to the piece can see what changed.

No hype, no hedging

The audience here builds and evaluates ML systems for a living. Copy that inflates capability or obscures limitation wastes their time. The goal is precision, not persuasion.