4 ms·

This article skirts around the issue, but the biggest (and most common) mistake I've seen machine learning startups make is starting with an algorithm and tryin

by exelius 11y ago

This article skirts around the issue, but the biggest (and most common) mistake I've seen machine learning startups make is starting with an algorithm and trying to productize it. When you do this, you end up with a solution looking for a problem, which is never a recipe for a successful startup. So in addition to finding a problem that fits your solution, you then also have to build a product around it. And chances are, if your product is really great, you can build it without fancy machine learning models and it would be just as successful. Machine learning, in my mind, is more of an enabler that makes great products more 'magical' - it's not really a product in itself. IMO it's a discipline that lends itself well to a consulting model - hire some ML wizards, unleash them along with a dev team on a problem for 6 months, then move on to the next one. This is mostly the "infiltrating the professions" - most consulting companies organize themselves around market verticals for the exact same reasons.

ThomPete 11y ago
Exactly! Unless you sell machine learning algorithms of course :)
- exelius 11y ago
  That's basically consulting :) There are very few truly novel applications of ML, and nearly all the major algorithms are open source. Using them together correctly and interpreting the output is the hard part -- which is perfect for scoping a consulting engagement.
nostrademons 11y ago
Also, the critical element for machine-learning models is training & testing data. Many startups lack access to large quantities of data; since data is proprietary and often expensive, it's really a competitive advantage for large established companies, not startups. If your startup is focused around a machine-learning model, your first priority should be to acquire lots of data - and usually, if you can acquire lots of data, you have a valuable product in its own right without the machine-learning. Totally agree about ML being an enabler to make great products better, not to bring new products into existence.

by exelius 11y ago

ThomPete 11y ago

Exactly! Unless you sell machine learning algorithms of course :)

exelius 11y ago
That's basically consulting :) There are very few truly novel applications of ML, and nearly all the major algorithms are open source. Using them together correctly and interpreting the output is the hard part -- which is perfect for scoping a consulting engagement.

nostrademons 11y ago

Also, the critical element for machine-learning models is training & testing data. Many startups lack access to large quantities of data; since data is proprietary and often expensive, it's really a competitive advantage for large established companies, not startups. If your startup is focused around a machine-learning model, your first priority should be to acquire lots of data - and usually, if you can acquire lots of data, you have a valuable product in its own right without the machine-learning. Totally agree about ML being an enabler to make great products better, not to bring new products into existence.