Machine learning software leverages algorithms that learn and adapt from data to automate complex decision-making and generate predictions, improving speed and accuracy of outputs over time as the application ingests more training data, with applications spanning process automation, customer service, security risk identification, and contextual collaboration.
Core Capabilities of Machine Learning Software
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
Common Use Cases for Machine Learning Software
Machine learning platforms are used across industries to power intelligent automation and predictive capabilities. Common use cases include:
- Automating complex decisions in financial services, healthcare, and agriculture
- Powering the backend AI that end users interact with in customer-facing applications
- Building and training models for security risk identification and fraud detection
How Machine Learning Software Differs from Other Tools
End users of machine learning-powered applications do not interact with the algorithm directly, machine learning powers the backend AI layer that users engage with. Machine learning platforms differ from machine learning operationalization (MLOps) platforms by focusing on model development and training rather than deployment monitoring and lifecycle management.
Insights from G2 on Machine Learning Software
Based on category trends on G2, flexible data ingestion and model accuracy improvements over time stand out as the most valued capabilities. Ease of integration with existing data infrastructure and the breadth of supported algorithms stand out as key decision factors.
