Opettaminen (training) on prosessi, jossa ML-malli opitaan datasta (opitaan säännönmukaisuudet, säädellään parametreja), kun taas päättely (inference) on opetetun mallin käyttäminen ennusteiden tekemiseen uusista datoista. Nämä ovat erillisiä vaiheita, joilla on erilaiset ominaisuudet ja kustannukset.
Opettaminen vs päättely
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)
