evaluation the fifth phase in the crisp-dm process takes place once we have the final models from the modeling phase that satisfy necessary success criteria with respect to our data mining goals and have the desired performance and results with regard to model evaluation metrics like accuracy the evaluation phase involves carrying out detailed assessment and review of the final models and the results which are obtained from them some of the main points that are evaluated in this section are as follows ranking final models based on the quality of results and their relevancy based on alignment with business objectives any assumptions or constraints that were invalidated by the models cost of deployment of the entire machine learning pipeline from data extraction and processing to modeling and predictions any pain points in the whole processwhat should be recommendedwhat should be avoideddata sufficiency report based on results final suggestionsfeedbackand recommendations from solutions team and smes based on the report formed from these pointsafter discussionthe team can decide whether they want to proceed to the next phase of model deployment or full reiteration is neededstarting from business and data understanding to modeling deployment the final phase in the crisp-dm process is all about deploying your selected models to production and making sure the transition from development to production is seamless usually most organizations follow standard path-to-production methodology proper plan for deployment is built based on resources requiredservershardwaresoftwareand so on models are validatedsavedand deployed on necessary systems and servers plan is also put in place for regular monitoring and maintenance of models to continuously evaluate their performancecheck for results and their validityand retirereplaceand update models as and when needed building machine intelligence the objective of machine learningdata miningor artificial intelligence is to make our lives easierautomate tasksand take better decisions building machine intelligence involves everything we have learned until now starting from machine learning concepts to actually implementing and building models and using them in the real world machine intelligence can be built using non-traditional computing approaches like machine learning in this sectionwe establish full-fledged end-to-end machine learning pipelines based on the crisp-dm modelwhich will help us solve real-world problems by building machine intelligence using structured process machine learning pipelines the best way to solve real-world machine learning or analytics problem is to use machine learning pipeline starting from getting your data to transforming it into information and insights using machine