machine learning with pipelines -machine automatic workflows introduction in order to execute and produce results successfullya machine learning model must automate some standard workflows the process of automate these standard workflows can be done with the help of scikit-learn pipelines from data scientist' perspectivepipeline is generalizedbut very important concept it basically allows data flow from its raw format to some useful information the working of pipelines can be understood with the help of following diagramdata ingestion data preparation ml model training model evaluation deployment ml model re-training the blocks of ml pipelines are as followsdata ingestionas the name suggestsit is the process of importing the data for use in ml project the data can be extracted in real time or batches from single or multiple systems it is one of the most challenging steps because the quality of data can affect the whole ml model data preparationafter importing the datawe need to prepare data to be used for our ml model data preprocessing is one of the most important technique of data preparation ml model trainingnext step is to train our ml model we have various ml algorithms like supervisedunsupervisedreinforcement to extract the features from dataand make predictions model evaluationnextwe need to evaluate the ml model in case of automl pipelineml model can be evaluated with the help of various statistical methods and business rules ml model retrainingin case of automl pipelineit is not necessary that the first model is best one the first model is considered as baseline model and we can train it repeatably to increase model' accuracy