out-of-core learning describes learning from data that cannot be stored in main memorybut where the learning takes place on single computer (or even single processor within computerthe data is read from source like the hard disk or the network either one sample at time or in chunks of multiple samplesso that each chunk fits into ram this subset of the data is then processed and the model is updated to reflect what was learned from the data thenthis chunk of the data is discarded and the next bit of data is read out-of-core learning is implemented for some of the models in scikit-learnand you can find details on it in the online user guide because out-of-core learning requires all of the data to be processed by single computerthis can lead to long runtimes on very large datasets alsonot all machine learning algorithms can be implemented in this way the other strategy for scaling is distributing the data over multiple machines in compute clusterand letting each computer process part of the data this can be much faster for some modelsand the size of the data that can be processed is only limited by the size of the cluster howeversuch computations often require relatively complex infrastructure one of the most popular distributed computing platforms at the moment is the spark platform built on top of hadoop spark includes some machine learning functionality within the mllib package if your data is already on hadoop filesystemor you are already using spark to preprocess your datathis might be the easiest option if you don' already have such infrastructure in placeestablishing and integrating spark cluster might be too large an efforthowever the vw package mentioned earlier provides some distributed features and might be better solution in this case honing your skills as with many things in lifeonly practice will allow you to become an expert in the topics we covered in this book feature extractionpreprocessingvisualizationand model building can vary widely between different tasks and different datasets maybe you are lucky enough to already have access to variety of datasets and tasks if you don' already have task in minda good place to start is machine learning competitionsin which dataset with given task is publishedand teams compete in creating the best possible predictions many companiesnonprofit organizationsand universities host these competitions one of the most popular places to find them is kagglea website that regularly holds data science competitionssome of which have substantial prize money attached the kaggle forums are also good source of information about the latest tools and tricks in machine learningand wide range of datasets are available on the site even more datasets with associated tasks can be found on the openml platformwhich where to go from here