we can always train the model on new data but then we would have to add new data samples along with the older historical training data and again re-build the model using this new batch of data if most of the model building workflow has already been implementedretraining model would not involve lot of efforthoweverwith the data size getting bigger with each new data samplethe retraining process will start consuming more processormemoryand disk resources over period of time these are some points to be considered when you are building models that would be running from systems having limited capacity online learning online learning methods work in different way as compared to batch learning methods the training data is usually fed in multiple incremental batches to the algorithm these data batches are also known as mini-batches in ml terminology howeverthe training process does not end there unlike batch learning methods it keeps on learning over period of time based on new data samples which are sent to it for prediction basically it predicts and learns in the process with new data on the fly without have to re-run the whole model on previous data samples there are several advantages to online learning--it is suitable in real-world scenarios where the model might need to keep learning and re-training on new data samples as they arrive problems like device failure or anomaly prediction and stock market forecasting are two relevant scenarios besides thissince the data is fed to the model in incremental mini-batchesyou can build these models on commodity hardware without worrying about memory or disk constraints since unlike batch learning methodsyou do not need to load the full dataset in memory before training the model besides thisonce the model trains on datasetsyou can remove them since we do not need the same data again as the model learns incrementally and remembers what it has learned in the past one of the major caveats in online learning methods is the fact that bad data samples can affect the model performance adversely all ml methods work on the principle of "garbage in garbage outhence if you supply bad data samples to well-trained modelit can start learning relationships and patterns that have no real significance and this ends up affecting the overall model performance since online learning methods keep learning based on new data samplesyou should ensure proper checks are in place to notify you in case suddenly the model performance drops also suitable model parameters like learning rate should be selected with care to ensure the model doesn' overfit or get biased based on specific data samples instance based learning there are various ways to build machine learning models using methods that try to generalize based on input data instance based learning involves ml systems and methods that use the raw data points themselves to figure out outcomes for newerpreviously unseen data samples instead of building an explicit model on training data and then testing it out simple example would be -nearest neighbor algorithm assuming we have our initial training data the ml method knows the representation of the data from the featuresincluding its dimensionsposition of each data pointand so on for any new data pointit will use similarity measure (like cosine or euclidean distanceand find the three nearest input data points to this new data point once that is decidedwe simply take majority of the outcomes for those three training points and predict or assign it as the outcome label/response for this new data point thusinstance based learning works by looking at the input data points and using similarity metric to generalize and predict for new data points