Use machine learning techniques such as clustering and classification in MATLAB^® to estimate the remaining useful life of equipment. Using data from a real-world example, we will explore importing, pre-processing, and labeling data, as well as selecting features, and training and comparing multiple machine learning models. We will show how MATLAB is used to build prognostics algorithms and take them into production, enabling companies to improve the reliability of their equipment and build new predictive maintenance services.