Machine Learning (ML)
Machine Learning (ML) is a subset of AI in which systems learn patterns from data and improve their performance on tasks without being explicitly programmed with rules for every case.
What Machine Learning Actually Is
Traditional software runs on explicit rules written by developers. Machine learning takes a different approach: developers specify the task and the data, and the model learns the underlying patterns from examples. The output is a model that can make predictions or decisions on new data based on what it learned.
ML predates the current generative AI wave by decades. Fraud detection systems, recommendation engines, image classifiers, and spam filters are all ML applications that have been in enterprise production for years.
ML and Modern AI
Modern LLMs and foundation models are ML systems, though of a fundamentally larger scale than earlier ML. The distinction between "AI" and "ML" in enterprise conversation often maps to newer generative and agentic systems versus older narrow ML systems, though technically all of them are ML.
See agentic AI vs generative AI for how the terminology has evolved.
Governing ML Systems
Traditional narrow ML models have their own governance requirements, particularly for bias, fairness, and explainability in regulated decision-making. Many enterprise regulations (fair lending, employment law, insurance underwriting) apply to narrow ML systems just as they apply to LLMs. Governance programs need to cover both categories consistently.
Related Terms
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