A first look at AI tools available in Amazon Web Services: Cost and Ease of Setup
26 September 2018

A first look at AI tools available in Amazon Web Services: Cost and Ease of Setup

Amazon’s AI philosophy is to make it easier for non-machine-learning experts to apply AI to solve business problems quickly and affordably.

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.

How much do Amazon AI services cost in the real-world? 

In general, Amazon Web Services (AWS) offers several ways to use a virtual machine (instance) in the cloud:

  • On Demand Instances — Rent an instance with specific capacity (CPU, memory, etc.). When the instance is no longer needed, you can power it down (“stop” it). You can “Start” it up later and pick up where you left off. Only running instances are billed.
  • Reserved Instances — You commit to using and paying for a number of instances for a certain period of time (usually 1–3 years). This is about 50% cheaper than On Demand Instances.
  • Spot instances — Use the spare computing capacity that Amazon has at any given moment. You bid on this available surplus capacity and usually get it for a good price. Spot instances can be applied to both On Demand and Reserved instances. The price of Spot instances varies, however, a P2 spot instances can be found at a 70–80% discount. 

In the real world the greatest AWS cost was actually running the instances. Storage costs came a distant second.

Summary

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.