COVID-19 has put the world in a period of prolonged crisis. Returning back to the workplace remains a distant goal for many businesses. To help companies survive this crisis, many companies are implementing AI & Machine Learning (ML) practices to drive more accurate and efficient business decisions in a time where technology is key to business continuity. Below we discuss what Machine Learning is all about, its application in business, and how the AWS ML stack can help simplify ML application development. 

The above video is a recording of Kris Skrinak's presentation during the Back to Business with AI event. Below is a recap.

What is Machine Learning?

Machine Learning is the study of computer algorithms that improve automatically through experience and can be summed up through one word: prediction. Unlike traditional programming in which results have some degree of uncertainty, ML ensures more accurate results. In Machine Learning, data is paramount and cloud technologies provide unprecedented data management capabilities. Consider sourcing, lineage, versioning, maintaining, cleaning and archiving of data - these actions can all be improved with Machine Learning. 

Use Cases of Machine Learning

With Deep Learning as a core subset of Machine Learning, we can see many practical use cases for ML in business. Here is the broadly categorized list:

To learn more about Machine Learning applications for your business, reach out to sales@vsoftconsulting.com. 

The AWS Machine Learning Stack

To ease development of Machine Learning applications faster and more efficiently, AWS offers a three-layered ML stack.

First Layer: This layer is called the AI Layer. AWS guides developers who may not have strong experience with creating Machine Learning applications and can also build Machine Learning models using AWS services. With the help of REST APIs, one can integrate these services into already existing apps.

Second Layer: The second layer is the Machine Learning Services. This layer introduces SageMaker Studio, which is an IDE that offers end-to-end integration. This layer permits the developers to train Machine Learning models based on the specific business use case data.

Third Layer: In the third layer, ML Framework and Infrastructure, AWS offers services that are tailored to developers with solid Machine Learning and data science expertise.