प्रशिक्षण डेटाबाट ML मोडेल सिकाउने प्रक्रिया हो (ढाँचा सिकना, प्यारामिटर समायोजन गर्ना), जबकि अनुमान प्रशिक्षित मोडेल नयाँ डेटामा भविष्यवाणी गर्न प्रयोग गर्ने हो। तिनीहरू भिन्न चरणहरू हुन् जसमा फरक फरक विशेषताहरू र लागतहरू छन्।
प्रशिक्षण बनाम अनुमान
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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)