Machine Learning as a Service (MLaaS) promises to put data science within the reach of companies. In that context, Amazon Machine Learning is a predictive analytics service with binary/multiclass classification and linear regression features. The service offers a simple workflow but lacks model selection features and has slow execution times. However, predictive performances are satisfying.
Data science is hot and sexy, but it is complex. Building and maintaining a data science infrastructure can be expensive. Experienced data scientists are scarce and in-house development of algorithms, building predictive analytics applications, and creating production-ready APIs, requires specific know-how and resources. Even though companies may anticipate the benefits of a data science service, they may not be ready to make the necessary investments without testing the waters first.
This is where Machine Learning-as-a-Service comes in with a promise to simplify and democratize Machine Learning: reap the benefits of Machine Learning within a short timeframe while keeping costs low.
I share my experience using the Amazon Machine Learning service in Amazon Machine Learning: Nice and Easy or Overly Simple?. This article was contributed to the Open Data Science blog.