Machine Learning as a Service

Amazon Machine Learning is an online service by Amazon Web Services (AWS) that does supervised learning for predictive analytics.

Launched in April 2015 at the AWS summit, Amazon ML joins a growing list of cloud-based machine learning services, such as Microsoft Azure, Google prediction, IBM Watson, Prediction IO, BigML, and many others. These online machine learning services form an offer commonly referred to as Machine Learning as a Service or MLaaS following a similar denomination pattern of other cloud-based services such as SaaS, PaaS, and IaaS respectively for Software, Platform, or Infrastructure as a Service.

Studies show that MLaaS is a potentially big business trend. ABI research, a business intelligence consultancy, estimates machine learning-based data analytics tools and services revenues to hit nearly $20 billion in 2021 as MLaaS services take off as outlined in this business report: http://iotbusinessnews.com/2016/08/01/39715-machine-learning-iot-enterprises-spikes-advent-machine-learning-service-models/

Eugenio Pasqua, Research Analyst at ABI Research, said the following:

"The emergence of the Machine-Learning-as-a-Service (MLaaS) model is good news for the market, as it cuts down the complexity and time required to implement machine learning and thus opens the doors to an increase in its adoption level, especially in the small-to-medium business sector."

The increased accessibility is a direct result of using an API-based infrastructure to build machine-learning models instead of developing applications from scratch. Offering efficient predictive analytics models without the need to code, host, and maintain complex code bases lowers the bar and makes ML available to smaller businesses and institutions.

Amazon ML takes this democratization approach further than the other actors in the field by significantly simplifying the predictive analytics process and its implementation. This simplification revolves around four design decisions that are embedded in the platform:

• A limited set of tasks: binary classification, multi classification and regression
• A single linear algorithm
• A limited choice of metrics to assess the quality of the prediction
• A simple set of tuning parameters for the underlying predictive algorithm

That somewhat constrained environment is simple enough while addressing most predictive analytics problems relevant to business. It can be leveraged across an array of different industries and use cases.