learning algorithms and techniques this is more of technical or solution based pipeline and it assumes that several aspects of the crisp-dm model are already coveredincluding the following points business and data understanding ml/dm technique selection riskassumptionsand constraints assessment machine learning pipeline will mainly consist of elements related to data retrieval and extractionpreparationmodelingevaluationand deployment figure - shows high-level overview of standard machine learning pipeline with the major phases highlighted in their blocks figure - standard machine learning pipeline from figure - it is evident that there are several major phases in the machine learning pipeline and they are quite similar to the crisp-dm process modelwhich is why we talked about it in detail earlier the major steps in the pipeline are briefly mentioned here data retrievalthis is mainly data collectionextractionand acquisition from various data sources and data stores we cover data retrieval mechanisms in detail in "processingwranglingand visualizing datadata preparationin this stepwe pre-process the dataclean itwrangle itand manipulate it as needed initial exploratory data analysis is also carried out next steps involved extractingengineeringand selecting features/attributes from the data data processing and wranglingmainly concerned with data processingcleaningmungingwrangling and performing initial descriptive and exploratory data analysis we cover this in further details with hands-on examples in "processingwranglingand visualizing datafeature extraction and engineeringherewe extract important features or attributes from the raw data and even create or engineer new features from existing features details on various feature engineering techniques are covered in "feature engineering and selection