What is the difference between training and inference in machine learning?

What is the difference between training and inference in machine learning?

Training and inference are two key stages in the machine learning process.

Training refers to the initial phase where a machine learning model is created or trained using a labeled dataset. During training, the model learns patterns and relationships within the data to make predictions or classifications. This involves optimizing the model's parameters and adjusting its internal weights to minimize the difference between predicted and actual outputs.

Inference, on the other hand, is the stage where the trained model is used to make predictions or classifications on new, unseen data. Inference involves applying the learned knowledge from the training phase to make accurate predictions or decisions. The trained model takes in input data and produces an output based on the patterns it has learned during training.

In summary, training is the process of teaching a machine learning model using labeled data, while inference is the stage where the trained model is used to make predictions or classifications on new, unseen data.