Marc Andreessen famously said that “software is eating the world.” The three horsemen of that march – Analytics, Big Data and Cloud are converging to create a new and exciting cloud marketplace – Machine Learning in the Cloud.

What is Machine Learning?

Machine Learning is a bit mystifying even for many of us in the software industry. Discussions about Machine Learning usually bring forth complex-sounding terms like Support Vector Machines, Naïve Bayesian Classifiers, and Hidden Markov Models – all which can be overwhelming if you do not have a background in computer science and math.

Well, you don’t need to know all such nitty-gritty of what goes on under the hood to appreciate Machine Learning use cases or explore applications in your industry/domain.

Machine Learning is simply a branch of computer science that uses advanced math andstatistics to build algorithms that can make predictions by analyzing existing data. These algorithms get better (i.e., they learn) as they crunch more and more data and receive feedback on the accuracy of their predictions.

Simply put,Machine Learning is a key piece of “predictive analytics” and Machine Learning in the cloud is a “Predictions Service” or “Predictions as a Service.”

What are the use cases?
Predictions can range a wide spectrum – from

• The simpler use cases (e.g., predicting whether an email is genuine or spam, or an online comment is spam or not) to
• The more complex like fraud detection (e.g., is a credit card transaction genuine or spurious?) and
• Shopping recommendations (which catalogitems to promote, whether to offer a discount or not)

This is just scratching the surface – the applications are virtually limitless.

Enterprise Applications

1. Traditionally, companies employed teams of data analysts and statisticians and bought (expensive) tools like SAS and SPSS to build predictive models. Prediction services in the cloud can upend that approach.
2. Nevertheless, while Machine Learning itself is not new, Machine Learning services in the cloud are relatively new and not very mature yet. But they offer you an interesting avenue to experiment. You can try out a few pilots and test your own readiness to integrate them into your use cases, technology architectures, and business processes.