Mike Singleton of FourSquare recently wrote a blog post entitled, “Algorithms as a Service”:

I think there’s a market opportunity to crease an AAS (algorithms as a service) company which provides simple APIs to implementations of common algorithms… Algorithms as a service would give you development efficiency, problem scalability (access to CPU farms), and confidence in the results.

Andrew chimed in with this:

I think what you’ve identified is that some APIs are about getting data into and out of an existing system that sort of lives on its own — e.g., Twitter’s, FourSquare’s, Flickr’s.

Then, other APIs are about abstracting certain problems and simplifying them to a simple API call. These are “algorithms as a service”.

So, in this category I put things like OpenCalais.com (entity extraction algorithms) and SimpleGeo.com (geolocation algorithms). I also put my own startup, Parse.ly, in this category; see https://parse.ly/p3 and https://parse.ly/api. For Parse.ly, what we’re doing is simplifying the following painful steps:

1) parsing and cleaning RSS/Atom feeds and other content sources in near-real-time
2) building personalized “resonance profiles” for different users that can be trained and queried
3) delivering personalized recommendations (Amazon/Netflix-style) of content to users, that can be listed, searched, and filtered

Our whole value proposition is that, yes, you could build algorithms to do personalized recommendations yourself and in-house, but it’s hard. There’s a lot of infrastructure that goes along with it. Your engineering team will spend months — not days — getting it right. So, why not just plug into our nice API instead?

I don’t think it needs a new name — it’s just an evolution of APIs and SaaS given the growing needs of developers to build more complex, dynamic applications and their increasing willingness to license best-of-breed 3rd-party platforms to do so.

January was an exciting month for Parse.ly. At the end of 2009, we were heads-down, polishing our own “algorithms-as-a-service” offering. We aligned our development around a public launch of it at the SIIA Information Industry Summit in NYC, where we were invited to present. Sachin gave a great presentation; here’s what one blogger had to say about it:

Parse.ly, a semantic tool that recommends content, steers users towards content towards personalization and recommendation through their licensed content. When and how [do] personalization really happen? […] Parse.ly collects a little personal interest information from users, “listens” to their content habits and provides recommendations that can be embedded in any number of content applications. Market segmentation data and other demographics fall out of this information naturally. Parse.ly is available to publishers now for integration via their new P3 platform.

At the same time as launching the Parse.ly Publisher Platform (P3), we also put online our API docs and made it possible for you get an API key. Then, we started conversations with some great brands in online / digital publishing (household names, even) about using our platform. These conversations have been going really well — almost too well! These companies know how much more valuable their online properties would be if they were built around engaging, personalized recommendations in the Amazon/Netflix style. And they have a lot of ideas about how to use the data and recommendations P3 will give them. We’ve already started to mock up new user interfaces for our API to make the integration with publishers as smooth as possible.

We’re excited for this new direction for Parse.ly. We agree with Mike that there are opportunities all around us to simplify algorithmically-tough problems to simple and highly-usable APIs. This will not only make web developers more productive, but it will also make the websites we use daily more useful and powerful!