ML vs. LLM. What is the difference?

Not all artificial intelligence (AI) is the same. Anyone who looks into the subject will quickly come across terms such as machine learning (ML) and large language models (LLM). But what exactly is behind these technologies, and which is suitable for which application?

What is machine learning (ML)?

Machine learning is a subfield of artificial intelligence in which an algorithm learns from existing data to recognize patterns and use them to predict future events or derive decisions. To do this, the model is fed with training data and learns through optimization which features are relevant for the result.

• ML uses structured data such as tables, numbers, or sensor values.
• The goal is to make accurate predictions or classify data.
• ML is based on statistics and model-based methods such as regression or decision trees.
• Typical application examples are forecasts, anomaly detection, classification, and regression models.

What is an LLM (Large Language Model)?

A large language model is a specialized AI language model that has been trained on billions of texts to understand, interpret, and generate new content. LLMs are based on so-called transformer architectures, which are capable of capturing context across large areas of text.

• LLMs are huge neural networks trained on extensive text corpora.
• The goal is to understand, interpret, and generate human language.
• LLMs are particularly strong at tasks such as text classification, summarization, question-answering systems, and chatbots.
• Well-known examples include GPT-4, Claude, Gemini, and Perplexity.

Machine learning is the better choice when structured data is available, such as numbers, tables, or measured values. When it comes to making accurate predictions, such as detecting anomalies or planning demand and resources, ML is often more efficient. ML also offers advantages when IT resources are limited, as the computing effort is usually lower.

LLMs are particularly useful when unstructured text needs to be analyzed or generated. They are suitable for understanding language, summarizing content, or writing new text. For applications with direct user interaction, such as chatbots or intelligent assistance systems, LLMs are usually the better choice.

Typical use cases for machine learning

• Quality monitoring in production environments
• Predictive maintenance and failure forecasts
• Segmentation and analysis of customer data
• Demand forecasts in retail and industry
• Fraud detection in insurance and financial systems

Typical use cases for LLMs

• Automated customer communication via chatbots
• Generation of product texts, reports, or emails
• Analysis and classification of large amounts of text data
• Summaries of meetings, emails, or support tickets
• Knowledge management through semantic search in documentation and FAQs