Related to a reply[0] I wrote in another thread. Most of the content out there is produced by people who never touched client data, sadly. A lot of enthusiastin

Related to a reply[0] I wrote in another thread. Most of the content out there is produced by people who never touched client data, sadly. A lot of enthusiasting/influencing/audience building/blogging on Medium and Twitter by data virgins, essentially. Those who are in the field are sadly not writing about it. I'd love to read their content. The other type of content is from "architects" and "thought leaders" who come up with acronyms and 60,000ft view with workflows and methodologies that make perfect sense if you never worked in the field. They seem to think they're the first to think of using "DevOps" in machine learning and they give naive examples on how to do that. This gets circulated. Even those who talk about topics such as "production machine learning" have clearly only worked on Kaggle competitions, then extrapolate that into advice to be applicable on real world data. Then there are companies that heavily use machine learning, but they have their own internal platforms which are not accessible to the general public. There are other companies that try to build such platforms, but they come into the problem without having much real world experience and tackling a problem they don't themselves have. These products tend to focus on stylesheets and animations, or whiteboard an ML workflow/pipeline that makes sense to them (but they've never actually done a project, so it's similar to writing the Kama Sutra without actually having "done the deed"). They'll tend to bake in a rigid workflow or what they think a pipeline is in the real world into their product. You can predict these will fail, and what sucks more than reading their post-mortems when they shut down is them having the wrong lessons learned.. We're not a large corporation, just a tiny consultancy, but we help large organizations with machine learning, and we meet them wherever they are on the maturity spectrum. Some, especially those who didn't view data as core to their operations, are in the "what's this AI thing and how can it help us". Others actually have internal machine learning and data science teams, but need help because their team is either fully booked, or we have expertise on a particular topic they don't have, and we augment their team for that project. Yet others are a pure research entities in artificial intelligence (the "AI" Skunkworks of a large organization) who talk with us to do it more effectively and solve their problems. The problems we work on are as diverse as our clients who are in different sectors and industries (energy, transporation, retail, banking, telecommunication, public relations, etc) and for different functions within the same organization when we do many projects with the same client, which is often. - [0]: https://news.ycombinator.com/item?id=25871632 https://news.ycombinator.com/item?id=25871632