Machine Learning as a Service gaining in popularity by those that matter - the implementers

I had the opportunity to speak on a panel last night on the topic of Machine Learning as a Service.  My fellow panelists were from BigML, Snap Analytx, and Grok.

One of the questions I was asked before the event was “What needs to happen for broader acceptance of Machine Learning as a Service?”  My answer is below:

“I think this hinges on a broader understanding of how intelligent algorithms like machine learning can add value.  This means greater discussion of successful use cases on how the use of these algorithms has a meaningful impact, especially as compared to traditional approaches.   I’m looking forward to the day when there are so many compelling stories that Amazon and Netflix aren’t one of the first examples people use to talk about Machine Learning.

I have noticed that an increasing number of people we talk to have spent some time educating themselves on this concept, and it’s not all techies.  Many are being exposed to the topic as part of the broader discussion around Big Data. Their analysis is evolving from  “How would I use this in my organization?” to “How do I fit this within the budget of my organizational constraints?” People are wondering if it is something they are going to build out internally or if there is a way for them to have access to some of the technology without building out internal resources. Those are the people that we really enjoy talking to.”