I run a company that specializes in design & implementation of kick-ass ML solutions [1]. We've had successful projects in quite a few industries at this point:

LEGAL INDUSTRY

Aka e-discovery [2]: produce digital documents in legal proceedings.

What was special: stringent requirements on statistical robustness! (the opposing party can challenge your process in court -- everything about way you build your datasets or measure the production recall the has to be absolutely bullet proof)

IT & SECURITY

Anomaly detection in system usage patterns (with features like process load, frequency, volume) using NNs.

What was special: extra features from document content (type of document being accessed, topic modeling, classification).

MEDIA

Built tiered IAB classification [3] for magazine and newspaper articles.

Built a topic modeling system to automatically discover themes in large document collections (articles, tweets), to replace manual taxonomies and tagging, for consistent KPI tracking.

What was special: massive data volumes, real-time processing.

REAL ESTATE

Built a recommendation engine that automatically assembles newsletters, and learns user preferences from their feedback (newsletter clicks), using multi-arm bandits.

What was special: exploration / exploitation tradeoff from implicit and explicit feedback. Topic modeling to get relevant features.

LIBRARY DISCOVERY

Built a search engine (which is called "discovery" in this industry), based on Elasticsearch.

What was special: we added a special plugin for "related article" recommendations, based on semantic analysis on article content (LDA, LSI).

HUMAN RESOURCES (HR)

Advised on an engine to automatically match CVs to job descriptions.

Built an ML engine to automatically route incoming job positions to hierarchy of some 1,000 pre-defined job categories.

Built a system to automatically extract structured information from (barely structured) CV PDFs.

Built a ML system to build "user profiles" from enterprise data (logs, wikis), then automatically match incoming help requests in plain text to domain experts.

What was special: Used bayesian inference to handle knowledge uncertainty and combine information from multiple sources.

TRANSPORTATION

Built a system to extract structured fixtures and cargoes from unstructured provider data (emails, attachments).

What was special: deep learning architecture on character level, to handle the massive amount of noise and variance.

BANKING

Built a system to automatically navigate banking sites for US banks, and scrape them on behalf of the user, using their provided username/password/MFA.

What was special: PITA of headless browsing. The ML part of identifying forms, pages and transactions was comparatively straightforward.

--------------

... and a bunch of others :)

Overall, in all cases, lots of tinkering and careful analysis to build something that actually works, as each industry is different and needs lots of SME. The dream of a "turn-key general-purpose ML" is still ways off, recent AI hype notwithstanding.

[1] http://rare-technologies.com/

[2] https://en.wikipedia.org/wiki/Electronic_discovery

[3] https://www.iab.com/guidelines/iab-quality-assurance-guideli...