Machine Learning
Also known as: ML
Machine Learning (ML) is a subfield of Artificial Intelligence where systems learn from data and improve without being explicitly programmed.
For such systems to run permanently in a company, the model alone is not enough. It takes data, governance and processes. How this becomes a permanent business function is described by netzstrategen’s AI Operations approach.
FAQ
What is the difference between Machine Learning and Artificial Intelligence? Artificial Intelligence is the umbrella term for machines that exhibit intelligent behavior. Machine Learning is the most important method beneath it: instead of programming rules explicitly, the system learns patterns from data.
What data does Machine Learning need? ML systems are only as good as the data that feeds them. Quality, representativeness and sufficient volume are decisive. Poor data quality is one of the most common reasons ML projects fail.
Is Machine Learning the same as Generative AI? No. Generative AI is a specific application of Machine Learning that creates new content such as text or images. ML also covers classic tasks such as classification, forecasting and pattern recognition.
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How this article was produced
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This article was produced with AI support. Ideation, editorial planning, substantive review and approval rest with a human; copy-editing sits with the AI. Editorial responsibility is held by André Hellmann.