juniorMLOps
What is the difference between training and inference?
Updated May 17, 2026
Short answer
Training builds the model; inference uses the trained model to make predictions.
Deep explanation
Training involves optimizing model parameters using labeled data and loss functions. Inference uses the frozen model to generate predictions on unseen data. Training is compute-heavy, while inference is latency-sensitive.
Real-world example
Training a recommendation model weekly, then serving predictions in real-time on an e-commerce app.
Common mistakes
- Using training pipeline logic during inference, causing inefficiency.
Follow-up questions
- Why is inference optimization important?
- Can training and inference use different infrastructures?