Amazon Machine Learning (AML) offers easy and highly-scalable on-ramp for interpreting data.
AML offers visual aids and easy-to-access analytics to make machine learning accessible to developers without a data science background, using the same technology fuelling Amazon's internal algorithms.
AML’s pay-as-you-go model helps businesses build machine learning models without having to create the code themselves.
You can use AML to "obtain predictions for your application using simple APIs, without having to implement custom prediction generation code, or manage any infrastructure," according to Amazon.
Amazon’s machine learning platform can "generate billions of predictions daily and serve those predictions in real-time and at high throughput," the company said.
In general, Amazon Web Services (AWS) offers several ways to use a virtual machine (instance) in the cloud:
In the real world the greatest AWS cost was actually running the instances. Storage costs came a distant second.
Overall the experience of trying Amazon Machine Learning (AML) left me feeling that it can be complicated Over time, as their product matures, I can see that Amazon will open up machine learning to a wider audience.
Today AI remains in the realm of specialists but AWS is getting closer to making AI accessible to the developer mass market.
Amazon’s AI philosophy is to make it easier for non-machine-learning experts to apply AI to solve business problems quickly and affordably.
Scalability at a good price is a distinct advantage of the Amazon Web Services machine learning toolbox. It is well worth exploring as an option for testing and building AI solutions.