Machine learning applications, especially those focused on classification, usually follow the same high-level workflow that's shown in the following diagram. The workflow is comprised of two phases—training the classifier and the classification of new instances. Both phases share common steps, as shown here:

First, we use a set of training data, select a representative subset as the training set, preprocess the missing data, and extract its features. A selected supervised learning algorithm is used to train a model, which is deployed in the second phase. The second phase puts a new data instance through the same preprocessing and feature extraction procedure and applies the learned model to obtain the instance label. If you are able to collect new labelled data, periodically rerun the learning phase to retrain the model and replace the old one with the retrained one in the classification phase.