OverviewWhat it is
Data science collects, cleans, and models data to make predictions or find structure. Machine learning is the modelling engine: instead of coding rules, an algorithm learns patterns from examples. This layer predates LLMs and still powers most real-world AI - pricing, fraud, churn, recommendations.
At a glanceData Science & ML Fundamentals
Most enterprise 'AI' still lives in this loop; an LLM is one option inside 'Train', not a replacement for it.
MechanicsHow it works
The workflow is a loop: gather data, clean it, engineer features, train a model, evaluate, deploy, and monitor for drift. Quality is dominated by data, not algorithms - garbage in, garbage out. The four learning paradigms (supervised, unsupervised, reinforcement, and the classical-vs-deep split) determine what a model can learn and what data it needs.
Ground levelWhat you actually build
A loop that starts before training and keeps running after deployment. An LLM is one option inside it, not a replacement for it.
LandscapeTypes & approaches
Click a highlighted type to open its own page — concept, use case, and diagram.
FeasibilityArchitecture & feasibility
Architecture & feasibility
- Feasibility starts with data: is there enough labelled, representative, legally usable data? No data, no model - this is the first thing an architect checks.
- Pipelines, feature stores, and monitoring for drift are the durable infrastructure. A model is a small part; the data platform around it is the system.
- Classical models are cheaper to serve, easier to explain, and simpler to certify - often the feasible choice over an LLM.
In practiceWhat it means for building
Not every problem needs GenAI. A simple classical model is often more accurate, explainable, and faster. Know when the answer is 'regression, not an LLM'.
Data quality, pipelines, and feature stores decide outcomes. Design for reliable, monitored data flows before choosing a model.
GlossaryKey terms
CheckCheck your understanding
When would you NOT use an LLM?
When the task is structured and well-defined (credit scoring, forecasting) - a classical model is cheaper, explainable, and often more accurate.
What makes an ML project succeed?
Data quality and a clear metric. Most failures are data and problem-framing issues, not model choice.
How do you know a deployed model is still good?
Monitor for data and concept drift against a live metric; retrain or roll back when it degrades.
What changedWhat changed here
Nothing in the daily brief has touched this page since 2026-09-25. The sweep runs every morning and checks every page on this site; when it finds something for this one, it lands here.
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