One of the criterion used to classify Machine Learning systems is whether or not the system can learn incrementally from a stream of incoming data. In batch learning, the system is incapable of learning incrementally: It must be trained using all the available data. This will generally take a lot of time and computing resources, so it is typically done offline, first the system is trained and then it's launched into production and runs without learning anymore; it just applied what it has learned. This is called offline learning. If you wish a batch learning system to know about new data, (such as a new type of spam), you will have to train a new version of the system from scratch on the full dataset (both new data and old data).