Review: 6 machine learning clouds

Amazon, Microsoft, Databricks, Google, HPE, and IBM machine learning toolkits run the gamut in breadth, depth, and ease

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What we call machine learning can take many forms. The purest form offers the analyst a set of data exploration tools, a choice of ML models, robust solution algorithms, and a way to use the solutions for predictions. The Amazon, Microsoft, Databricks, Google, and IBM clouds all offer prediction APIs that give the analyst various amounts of control. HPE Haven OnDemand offers a limited prediction API for binary classification problems.

Not every machine learning problem has to be solved from scratch, however. Some problems can be trained on a sufficiently large sample to be more widely applicable. For example, speech-to-text, text-to-speech, text analytics, and face recognition are problems for which "canned" solutions often work. Not surprising, a number of machine learning cloud providers offer these capabilities through an API, allowing developers to incorporate them in their applications.

These services will recognize spoken American English (and some other languages) and transcribe it. But how well a given service will work for a given speaker will depend on the dialect and accent of the speaker and the extent to which the solution was trained on similar dialects and accents. Microsoft Azure, IBM, Google, and Haven OnDemand all have working speech-to-text services.

There are many kinds of machine learning problems. For example, regression problems try to predict a continuous variable (such as sales) from other observations, and classification problems attempt to predict the class into which a given set of observations will fall (say, email spam). Amazon, Microsoft, Databricks, Google, HPE, and IBM provide tools for solving a range of machine learning problems, though some toolkits are much more complete than others.

In this article, I'll briefly discuss these six commercial machine learning solutions, along with links to the five full hands-on reviews that I've already published. Google's announcement of cloud-based machine learning tools and applications in March was, unfortunately, well ahead of the public availability of Google Cloud Machine Learning.

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