An awesome-list for ML that remembers industries exist

What it does

This is a curated index of applied machine learning and data science notebooks and libraries organized by industry vertical—from accommodation and agriculture to utilities and wholesale. The catalogue, inspired by the awesome-machine-learning format, links out to Python projects (mostly Jupyter notebooks) that solve domain-specific problems like food-inspection forecasting, genomics sequencing, or manufacturing-failure prediction. It also tacks on a career section listing job platforms and employer-review sites.

The interesting bit

The breadth is the point. Rather than clustering around the usual finance and ad-tech examples, the list deliberately mines niche verticals such as emergency relief, justice and law, and material science. The maintainer also uses the README as a billboard for Sov.ai, their quantitative-finance research firm, which makes the repo feel like half field guide, half recruitment poster.

Key highlights

• Covers 15+ industries with sub-categorized links (e.g., fraud detection under Banking, genomics under Biotech)
• Primarily Python and Jupyter notebooks unless otherwise noted
• Accepts contributions via pull request, Google Sheet, or a dedicated subreddit
• Explicit deprecation policy: repos are flagged if unmaintained or idle for 2–3 years
• 7,486 stars suggest it fills a reference gap for domain-specific practitioners

Caveats

• The README itself cautions that the list is a “work in progress” and actively seeks maintainers for several sparse sections
• The top half of the page is dominated by Sov.ai recruitment copy, so you have to scroll to reach the actual links
• Because it is purely a directory, code quality and freshness of the linked repositories vary

Verdict

Worth bookmarking if you are a data scientist looking for vertical-specific inspiration or trying to break out of generic Kaggle territory. Skip it if you want a polished, self-contained library rather than a curated link farm.