🚀 The three ages of Data Science: Traditional ML, Deep Learning or LLM?

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The evolution of machine learning has completely changed how we solve problems. This article brilliantly explains when to use each approach depending on the complexity of the case:

• 🤖 Traditional Machine Learning → fast, cheap, and perfect for clean, structured data.
• 🧠 Deep Learning (BERT, embeddings) → ideal when language is ambiguous or contextual.
• 🌐 LLMs (GPT and similar) → powerful when there are no labeled examples or when the problem demands advanced understanding.

Each technology has its place. You don’t always need to “use a cannon to kill a fly.”

🧩 In a nutshell

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Imagine you want to classify the sentiment of a text:

• Traditional ML works well if the sentences are simple and direct.
• Deep Learning understands sarcasm, double meanings, and nuance better.
• LLMs can classify even without being trained on prior examples.

The key is to choose the tool based on accuracy, speed, and cost.

More information at the link 👇

The Three Ages of Data Science: When to Use Traditional Machine Learning, Deep Learning, or a LLM (Explained with One Example) | Towards Data Science

A practical use case to describe how the data scientist job changed across three generations of machine learning

towardsdatascience.com ↗