deploymentat lastwe need to deploy the model this step involves applying and migrating the model to business operations for their use challenges accompanying ml pipelines in order to create ml pipelinesdata scientists face many challenges these challenges fall into the following three categoriesquality of data the success of any ml model depends heavily on the quality of data if the data we are providing to ml model is not accuratereliable and robustthen we are going to end with wrong or misleading output data reliability another challenge associated with ml pipelines is the reliability of data we are providing to the ml model as we knowthere can be various sources from which data scientist can acquire data but to get the best resultsit must be assured that the data sources are reliable and trusted data accessibility to get the best results out of ml pipelinesthe data itself must be accessible which requires consolidationcleansing and curation of data as result of data accessibility propertymetadata will be updated with new tags modelling ml pipeline and data preparation data leakagehappening from training dataset to testing datasetis an important issue for data scientist to deal with while preparing data for ml model generallyat the time of data preparationdata scientist uses techniques like standardization or normalization on entire dataset before learning but these techniques cannot help us from the leakage of data because the training dataset would have been influenced by the scale of the data in the testing dataset by using ml pipelineswe can prevent this data leakage because pipelines ensure that data preparation like standardization is constrained to each fold of our cross-validation procedure example the following is an example in python that demonstrate data preparation and model evaluation workflow for this purposewe are using pima indian diabetes dataset from sklearn firstwe will be creating pipeline that standardized the data then linear discriminative analysis model will be created and at last the pipeline will be evaluated using -fold cross validation firstimport the required packages as followsfrom pandas import read_csv from sklearn model_selection import kfold from sklearn model_selection import cross_val_score