This chapter is from the book
This chapter is from the book
This chapter is from the book
Inference Models
In AI and machine learning, inference is the step where the model is fully trained and deployed, when it applies the relationships that were learned from the training phase. It allows the model to make predictions or classification on new data it has not seen before, allowing it to infer an output result solely based on the learned parameters and a given input.
Once a model is deployed for inference, it does not typically receive further training. However, it is still useful to continually monitor how the model is performing compared with real-world observations. When differences become noticeable, further training, fine-tuning, or other adjustments to the model may be necessary. Keeping an eye on the accuracy of your model is thus a central part of the AI development cycle.
While not all AI models are generative, almost all of them rely on this same training–inference cycle. However, the complexity of the model, the size of the dataset, and the sophistication of the outputs differ from one model to another.
