Incremental learning is used to continuously update and tune an existing model, as more data points come in. However, there is a tradeoff between speed and accuracy as the model becomes stable. From a big data angle, computing machine learning models is challenging when data sets cannot fit in main memory or when they exceed CPU capacity. On the other hand, data summarization is a fundamental technique that has promise to accelerate data science computations and compress a data set. Keeping these motivations in mind, we present an innovative system, VIM, that computes machine learning models in an incremental manner, visualizing continuous learning of model parameters as the data set is scanned. Our system is fast, it works for a wide spectrum of machine learning models and it can handle data sets larger than main memory. We developed an intuitive GUI which: (1) guides the user to upload data sets and choose a machine learning model, (2) offers interactive visualization of model parameters, and (3) helps getting an approximate model, stopping early, without reading the whole data set.