Machine learning software automates tasks for users by leveraging an algorithm to produce an output. These solutions are typically embedded into various platforms and have use cases across a wide variety of industries. Machine learning solutions improve the speed and accuracy of desired outputs by constantly refining them as the application digests more training data. Machine learning software improves processes and introduces efficiency to multiple industries, ranging from financial services to agriculture. Machine learning applications include process automation, customer service, security risk identification, and contextual collaboration.
Notably, end users of machine learning-powered applications do not interact with the algorithm directly. Rather, machine learning powers the backend of the artificial intelligence (AI) that users interact with. Some prime examples of this include chatbots software and automated insurance claims management software
To qualify for inclusion in the Machine Learning category, a product must:
Offer an algorithm or product that learns and adapts based on data
Be the source of intelligent learning capabilities for applications
Consume data inputs from a variety of data pools
Provide an output that solves a specific issue based on the learned data