feature scaling and selectiondata features often need to be normalized and scaled to prevent machine learning algorithms from getting biased besides thisoften we need to select subset of all available features based on feature importance and quality this process is known as feature selection "feature engineering and selection,covers these aspects modelingin the process of modelingwe usually feed the data features to machine learning method or algorithm and train the modeltypically to optimize specific cost function in most cases with the objective of reducing errors and generalizing the representations learned from the data "buildingtuningand deploying models,covers the art and science behind building machine learning models model evaluation and tuningbuilt models are evaluated and tested on validation datasets andbased on metrics like accuracyf scoreand othersthe model performance is evaluated models have various parameters that are tuned in process called hyperparameter optimization to get models with the best and optimal results "buildingtuningand deploying models,covers these aspects deployment and monitoringselected models are deployed in production and are constantly monitored based on their predictions and results details on model deployment are covered in "buildingtuning and deploying modelssupervised machine learning pipeline by now we know that supervised machine learning methods are all about working with supervised labeled data to train models and then predict outcomes for new data samples some processes like feature engineeringscalingand selection should always remain constant so that the same features are used for training the model and the same features are extracted from new data samples to feed the model in the prediction phase based on our earlier generic machine learning pipelinefigure - shows standard supervised machine learning pipeline figure - supervised machine learning pipeline you can clearly see the two phases of model training and prediction highlighted in figure - alsobased on what we had mentioned earlierthe same sequence of data processingwranglingfeature engineeringscalingand selection is used for both data used in training the model and future data samples for which the model predicts outcomes this is very important point that you must remember whenever you are building any supervised model besides thisas depictedthe model is combination of machine