Farm equipment manufactured by Blue River Technology uses machine learning to spray cotton crops.

Machine Learning Access Easy with 5 Open Sources

Developers are using machine learning now more than ever because of the ease of accessing open source framework libraries, according to a story on by Michael Georgiou, Co-Founder and CMO of Imaginovation.

Machine learning is using AI to teach systems to learn automatically and to improve with experience. It’s currently employed to teach digital assistants like Alexa and chatbots, for detecting security threats and fraud, identifying plant locations and teaching farm machines where to spray them, for marketing analysis, including writing personalized sales letters, among other uses. Generally, it can give companies the advantage of greater responsiveness.

Framework, Georgiou explained, is the programs, libraries and languages used in application development. A library is a collection of objects or methods used by an application. Here are the five frameworks outlined:


Machine learning software readily available includes TensorFlow, developed by the Google Brain Team to organize perceptual and language accessing tasks. It’s one of the most versatile frameworks, allowing users to write their own libraries, and it can run in the cloud and on mobile computing platforms.

Amazon Machine Learning

Built for developers, it has tools to help build models without working from the bottom up. Its advantage is access to Amazon Redshift, the data warehouse platform as a service.


State-of-the-art algorithms and data structures are part of its appeal. It can also run on Windows, Linux and MacOS.


The NET machine learning  framework offers multiple libraries handle pattern recognition, image and signal processing, statistical data processing and more.

Apache frameworks

Apache Signa, SparkMLlib and Mahout all are used in natural language processing and image recognition.