On April 27th our cloud solutions architect Laith Al-Saadoon was featured in TechTarget’s Search AWS article discussing Amazon Machine Learning. An excerpt of the article can be found below.
Machine learning use cases add up in AWS’ favor
by Alan R. Earls | TechTarget
Creating machine learning models was once a complicated task, best left to mathematicians. But AWS and several other companies are making the technology accessible to enterprises.
Amazon Machine Learning is an AWS Cloud service that offers visualization tools and wizards to help developers create models for machine learning use cases. From there, it’s a relatively straightforward matter to pull predictions out of an application using simple APIs. Amazon and some of its competitors are taking a once challenging field that required special programming and math skills and making it accessible to almost anyone.
The services build on the algorithms that Amazon data scientists and the Amazon e-commerce business used internally, explained Laith Al-Saadoon, cloud solutions architect at CorpInfo-Onica, an AWS premier consulting partner based in Santa Monica, CA. These algorithms support billions of predictions daily in both real-time and batch operations.
The service has a pay-as-you-go approach, so there are no upfront licenses or commitments. You pay per request, and avoid having to make hardware or software commitments, Al-Saadoon noted. In his estimation, the learning curve is not steep. As long as an administrator or data analyst understands the AWS ecosystem – such as Kinesis, Redshift and Relational Database Service – there is very little ramp up. IT teams can improve the pace of exploration and experimentation by using Amazon Machine Learning, thereby reducing time to market, he added.
Machine learning use cases and competition
Machine learning – Amazon Machine Learning, in particular – is a step up from traditional analytics because it focuses on predictive and potentially prescriptive results. Amazon Machine Learning is not as sophisticated as some other machine learning options. For instance, training data sets are limited in size to 100 gigabytes, and actual batch prediction data sets are limited to 1 terabyte.
Certainly there are many alternatives. Perhaps the best known is IBM Watson Analytics, which provides data visualization and predictive analytics with a simple “conversational” interface. Microsoft Azure Machine Learning Studio offers a library of ready-to-use examples that can be adapted for many machine learning use cases. Regardless, Al-Saadoon is enthusiastic about Amazon and said the uses of the offering are only limited by one’s imagination.
Machine learning – Amazon Machine Learning, in particular – is a step up from traditional analytics because it focuses on predictive and potentially prescriptive results.
For example, most organizations want to have a better understanding of user behavior on their website to optimize their visits or hold users’ attention. Machine learning and predictive analytics can analyze clickstream data using Simple Storage Service (S3) or Redshift and develop predictions about where users are likely to click. Based on those probabilities, admins or data analysts work to push those clicks to a preferred end state, such as heading toward the checkout.
Amazon Machine Learning also has potential to be used as a recommendation engine. “Any vertical can benefit from recommendation engines. For example in the food services and reviews industry, an app built using Amazon Machine Learning could recommend what restaurant a customer should visit,” Al-Saadoon said. In this example, a user might enter her age, gender and location, and the machine learning capability would compare the data to similar individuals, reviews from those individuals and similar data to come up with businesses that fit the profile. The process can be applied to a wide range of customer decisions.
To read the full article, please visit TechTarget.