Machine learning software leverages algorithms to automate complex decision-making and generate predictions, eliminating the need for manual rule configuration. 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 in multiple industries, ranging from financial services to agriculture. Common 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. Instead, machine learning powers the backend of the artificial intelligence (AI) that users interact with. Machine learning platforms function differently from machine learning operationalization (MLOps) platforms by focusing on model development and training rather than deployment monitoring and lifecycle management.
To qualify for inclusion in the Machine Learning category, a product must:
Offer an algorithm that learns and adapts based on data
Consume data inputs from a variety of data pools
Ingest data from structured, unstructured, or streaming sources, including local files, cloud storage, databases, or APIs
Be the source of intelligent learning capabilities for applications
Provide an output that solves a specific issue based on the learned data
