Training
The one-time, expensive process of building a model by exposing it to huge amounts of data until it learns patterns.
Training happens rarely and costs a fortune; using the finished model (inference) is what you do daily.
Frequently asked questions
What is the difference between training and fine-tuning?
Training builds a model from scratch on huge amounts of data, which is rare and hugely expensive. Fine-tuning takes an already-trained model and nudges it with your own examples to adopt a specific style or specialty, which is far cheaper and more common.
Is training the same thing as using an AI?
No. Training happens once to build the model, while inference is every time you use the finished model afterward. You pay for training rarely, if ever, but you pay for inference each time you send a request.
Why is training an AI model so expensive?
It means feeding the model enormous amounts of data over a long time on powerful hardware until it learns patterns. That one-time process can cost a fortune, which is why most businesses use a pre-trained model rather than training their own.
New to all this? Start with what an AI agent really is, browse the full glossary, or explore the learning hub.
← Back to the glossary