Automating Machine Learning Workflows

Machine Learning (ML) services are quickly becoming a taken-for-granted part of the software developer's toolbox, in any domain. These days, databases or networking are a standard component of almost any non-trivial application, so easily integrated that almost no special expertise is required. We expect to see Machine Learning becoming, in the very near future, a similar layer in the software stack. This commoditization of ML services has been driven so far by Service-oriented platforms such as BigML, which have provided a key ingredient of the process: abstraction. Simple and easy to use REST APIs hide away not only the details of the sophisticated algorithms underlying the services at hand, but also the complexities of scaling those computations both over CPU cycles and input data volumes.