This article skirts around the issue, but the biggest (and most common) mistake I&#x27;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.<p>Machine learning, in my mind, is more of an enabler that makes great products more &#x27;magical&#x27; - it&#x27;s not really a product in itself. IMO it&#x27;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 &quot;infiltrating the professions&quot; - most consulting companies organize themselves around market verticals for the exact same reasons.