I recently had someone ask me why Machine Learning as a Service is a viable business. It’s not the first time I’ve heard that question. Below is an excerpt from my response:
“I think one of the best trends to look at is the increase in number of APIs that are available – and the increasing number of applications that are mashups of various APIs. Some of these mashups (i.e. Summly) have been very successful.
With the web now highly programmable, basic functions are now commodities (payments, social, location, etc). As this happens the standard for all applications rises, and developers look to new technologies to add competitive differentiation to their applications.
We believe Machine Learning will be a key competitive differentiator for applications. Being able to turn raw data into intelligent output to build better apps and user experiences has already shown to be a significant advantage (Google, Netflix, Amazon). The same artificial intelligence that helps make these companies great, must eventually be delivered in a way that the broader market can consume because it will demand it to stay competitive.
We’re still early on in the adoption cycle, most of the developers I’ve talked too today would rather consume ML inside of a complete application, but we’re starting to see an increase in the number of new inquiries we receive about specific algorithms. Developers and companies are realizing that if they don’t begin to do more with their data they will be left behind by competitors. Not all of them are going to learn or hire someone who know ML, so having access to it via a simple API can be an attractive option”
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