Antrenarea este procesul de a preda unui model de ML din date (învățarea de modele, ajustarea parametrilor), în timp ce inferența este utilizarea modelului antrenat pentru a face predicții pe date noi. Sunt faze distincte cu caracteristici și costuri diferite.
Antrenare vs inferență
TRAINING → teaching the model (the LEARNING phase):
→ feed lots of DATA → the model adjusts its parameters to learn patterns
→ computationally EXPENSIVE (lots of data, compute, time — e.g. training an LLM costs
huge resources); done once (or periodically to update)
→ produces a trained MODEL
INFERENCE → using the trained model (the PREDICTION phase):
→ give the trained model NEW input → it produces an output (prediction/generation)
→ much CHEAPER/faster than training (a single forward pass); done MANY times (every
time you use the model)
→ train once (expensive), infer many times (cheaper, in production)
