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It's hard to find articles like this that give a glimpse into what is used by larger shops doing ML. I take this one with a grain of salt due to the source bein
by natch 6y ago
It's hard to find articles like this that give a glimpse into what is used by larger shops doing ML. I take this one with a grain of salt due to the source being a vendor, but still it is generous with the amount of detail and with its even mentioning some alternative solutions for cases that might fit those, so that is really appreciated.
The pros working in big shops who write these tend to overlook the tiny use cases such as apps that recognize a cat coming through a cat door (as opposed to a raccoon) which can get by with minuscule training.
There's a lot of discussion of "big data" but small data is amazingly powerful too. I wish there was more bridging of these two worlds — to have tools that deal with the needs of small data, without the assumption that training a model takes days or months, and on the other side, to have the big data world share more insights about how they manage their data for the big cases. There is a ton of info out there but what I find lacking is info about how labeling and tagging is managed on a large scale (I'm interested in both, big and small, as well as medium). Maybe I'm just missing something. This article gave some good clues — thanks!
- nailer 6y agoI feel that too - I joined HL just a couple of weeks ago (from Unix/webdev tech focus) and it's been a lot of learning so far. I'm going to do a little research (and write a blog post) into the specific case of 'too subtle for a regex' aimed at general webdev folk who have a problem to solve rather than people that already want to use ML.
- fttx_ 6y agoI agree. I’ve actually been working on something along these lines[0], albeit with a focus on marketing analytics. In my experience everyone in marketing cares about dashboards and metrics a lot but outside of larger shops almost no-one is doing any real analysis and even simple tools like linear regression could have a big impact. Early days but we’re looking to onboard a few more customers to help guide our roadmap. [0] https://ripbase.com https://ripbase.com
- antognini 6y ago> There's a lot of discussion of "big data" but small data is amazingly powerful too. You can oftentimes do surprisingly well on a smaller dataset with proper augmentation. Unfortunately augmentation techniques don't seem to get quite as much attention as they deserve, perhaps because they're perceived in ML conferences as problem-specific hacks rather than general-purpose techniques.