Both IBM and Microsoft are attempting to create two different flavors of machine learning services. One's been created behind closed doors, as it were, with a curated data set and tuned behaviors (the Watson APIs, Project Oxford). The other is a platform upon which new kinds of machine learning services can be built, shared, and even monetized (Azure Machine Learning Studio, Predictive Analytics).
But the biggest difference between Microsoft and IBM isn't in the services, but the motivations. Microsoft's attempts at future-proofing itself by pivoting to the cloud have been aided by its other successful business sectors -- gaming, for instance -- so it hasn't felt existential pressure of the same degree that IBM has. But that doesn't mean Microsoft can't sense which way things must go.
Amazon and Google, the minimalists
If Google and especially Amazon have any one guiding tenet to their cloud approaches, it's "less is more." Maybe better to say "just enough is more," which includes the way both companies offer cloud-based machine learning services.
In Google's case, Google Cloud Platform currently offers only two services akin to the others profiled here: Google Translate (an API supporting Google's existing machine translation engine), and Google Prediction API. The former is a proprietary API maintained exclusively by Google. The latter, despite the unassuming name, is a broadly inclusive service that allows users to upload data and train models in the manner of of Microsoft Azure Machine Learning Studio. (Data can be derived from Google services like Google BigQuery.)
Amazon Machine Learning is similar to Google Prediction API in that models can be trained against data and used to make predictions. It's a deliberately simplified service, either for the sake of appealing to developers who only want to solve a specific, narrow problem or because Amazon wanted to test the market waters first.
In both Amazon and Google's cases, their targets are developers both with narrowly defined needs and with data already on those clouds -- the "just enough" model. IBM and Microsoft are aiming for far broader territory, and while IBM strives to have the most to offer, it also has the most to lose.
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