Question 1

What's the difference between model training and model inference?

Accepted Answer

Model training is when you teach a model by learning from data and adjusting its parameters. Model inference is when you use the trained model to make predictions on new data (for example, classifying a new email as spam or not spam). Training is usually compute-heavy and done periodically; inference is typically optimized for low latency and runs continuously in production.

Question 2

When should I use model training instead of a pre-trained model?

Accepted Answer

Use model training when you need a model tailored to your data or task (for example, your company’s specific product catalog, documents, or fraud patterns), when accuracy with your domain data matters, or when you must meet specific requirements (language, compliance, explainability). Use a pre-trained model when your task is common and the pre-trained model already performs well, or when you want faster time-to-value and lower training cost.

Question 3

How much does model training cost in the cloud?

Accepted Answer

Costs mainly depend on (1) compute type and duration (CPU vs GPU/accelerators, number of nodes, hours used), (2) data storage and I/O (reading large datasets from object storage), (3) networking (data transfer between regions/services), and (4) MLOps tooling (experiment tracking, artifact storage). Training deep learning models on GPUs can be significantly more expensive than classical ML on CPUs. To control cost, teams commonly use spot/preemptible instances where supported, right-size instance types, use early stopping, and reduce training data or model size when appropriate.

Model Training

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