Next Up: Machine Learning on AWS
If you have been to AWS re:Invent, then you know the tremendous amount of excitement that cloud evangelists experience during that time of the year.
The events that AWS hosts in Las Vegas provide a surreal experience for first-timers and are sure to excite even the most seasoned of veterans. Let’s talk about one of the exciting technologies that are sure to change the world as we know it, or at least the businesses we are familiar with – Amazon Machine Learning.
Introduced on April 9, 2015, Amazon Machine Learning (ML) has received a surge of attention in recent years given its capability to provide highly reliable and accurate predictions with a large dataset. From using Amazon ML to track next-generation stats in the NFL, to analyzing real-time race data in Formula 1, to enhancing fraud detection at Capital One, ML is changing the way we share experiences and interact with the world around us.
During re:Invent 2018, AWS made it clear that ML is here to stay and has announced many offerings that support the development of ML solutions or services. But you may be wondering: What exactly is Amazon ML?
According to AWS’s definition:
“Amazon Machine Learning is a machine service that allows you to easily build predictive applications, including fraud detection, demand forecasting, and click prediction. Amazon Machine Learning uses powerful algorithms that can help you create machine learning models by finding patterns in existing data and using these patterns to make predictions from new data as it becomes available.”
We, as a society, are at the point where machines are actively providing decisions for many of our day-to-day interactions with the world. If you’ve ever shopped as a Prime member on Amazon.com, you have already experienced an ML algorithm that is in tune with your buying preferences.
In our Engineer’s Corner, our very own Kris Brandt Amazon Web Service As A Data Lake, discusses the critical initial step towards implementing an ML project, Data Lake creation. In this blog, Kris explores what a Data Lake is and provides some variations to its implementation. The development of a robust data lake is requisite for implementing an ML project that provides the business value expected from the service capabilities. ML runs on data and having plenty of it provides a foundation for an exceptional outcome.
Utilizing existing data repositories, we can work with business leaders to develop those cases for leveraging the data and the ML for strategic growth. You can connect with the Effectual team by emailing firstname.lastname@example.org.
Because of ML’s proliferation throughout the market, AWS announced these ML solution opportunities during re:Invent 2018:
AWS Lake Formation
“This fully managed service will help you build, secure, and manage a data lake,” according to AWS. It allows you to point it at your data sources, crawl the sources, and pull the data into Amazon Simple Storage Service (S3). “Lake Formation uses Machine Learning to identify and de-duplicate data and performs format changes to accelerate analytical processing. You will also be able to define and centrally manage consistent security policies across your data lake and the services that you use to analyze and process the data,” says AWS.
“This Optical Character Recognition (OCR) service will help you to extract text and data from virtually any document. Powered by Machine Learning, Amazon Textract will identify bounding boxes, detect key-value pairs, and make sense of tables, while eliminating manual effort and lowering your document-processing costs,” according to AWS.