Identifying candidate problems that can be solved using ML

It is also important for data scientists to be able to understand the scale of data that they are working with. There might be tasks related to medical research that span thousands of patients, with hundreds of features that can be processed on a single node device. However, tasks such as advertising, where companies collect several petabytes of data on customers based on every online advertisement that is served to the user, may require several thousand machines to compute and train ML algorithms. Deep learning algorithms are GPU-intensive and require a different type of machine than other ML algorithms. In this book, for each algorithm, we supply a description of how it is implemented simply using Python libraries, and then, how it can be scaled on large AWS clusters using technologies such as Spark and AWS SageMaker...