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 (this is compute-intensive and can take minutes to days). Model inference is when you use the already-trained model to make predictions on new data (usually optimized for low latency and high throughput).

Question 2

When should I use Model Training?

Accepted Answer

Use model training when you need a model that can predict, classify, recommend, or generate outputs based on patterns in data—especially if rules are hard to write by hand. You typically retrain when you have enough new data, when performance drops (data drift), when you add new labels/classes, or when you want to improve accuracy with better features or algorithms.

Question 3

How much does Model Training cost?

Accepted Answer

Costs mainly come from compute (CPU/GPU/accelerator hours), storage (datasets, checkpoints, model artifacts), and data processing (ETL/feature engineering). Price varies by instance type, training duration, distributed training scale, and how often you retrain. To control cost, use smaller instances for prototyping, spot/preemptible capacity when appropriate, early stopping, right-sized batch sizes, and scheduled retraining instead of continuous retraining.

Model Training

Definition

Real-World Example

Related Terms

Cloud Provider Equivalencies

Explore More Cloud Computing Terms