model deployment as service the computational world is seeing surge of the cloud and the xaas (anything as servicemodel in all areas this is also true for model development and deployment major providers like googlemicrosoftand amazon web services (awsprovide the facility of developing machine learning models using their cloud services and also the facility of deploying those models as service on the cloud this is very beneficial to the end users due to the reliability and ease of scaling offered by these service providers major downside to custom development or deploying models in-house is the extra work and maintenance required the scalability of the solution is also another problem that may exist for some kind of models like fraud predictiondue to the sheer number of prediction volumes required model deployment as service takes care of these issues as in most cases the model prediction can be accessed via request made to cloud based api endpoint (by supplying in the necessary data of coursethis capability frees the burden of maintaining an extra system for the developers of the application that will be consuming the outputs of our model in most casesif the developers can take care of passing the required data to the model deployment apisthey don' have to deal with the computational requirement of the prediction system and dealing with its maintenance another advantage of cloud deployment comes from how easy it is to update the models model development is an iterative process and the deployed models need to be updated from time to time to maintain their relevance by maintaining the models at single end point in the cloudwe simplify the process of model updating as only single replacement is requiredwhich can actually happen with the push of buttonwhich also syncs with all downstream applications summary this concludes the second part of this bookwhich focused on the machine learning pipeline we learned the most important aspects of the model building processwhich include model trainingtuningevaluationinterpretationand deployment details of various types of models was discussed in the model building section including classificationregressionand clustering models we also covered the three vital stages of any machine learning process with an example of the logistic regression model and how gradient descent is an important optimization process hands-on examples of classification and clustering model building processes were depicted on real datasets various strategies of evaluating classificationregressionand clustering models were also covered with detailed metrics for each of themwhich were depicted with real examples section of this book has been completely dedicated to tuning of models that include strategies for hyperparameter tuning and cross validation with detailed depiction of tuning on real models nascent field in machine learning is model interpretationwhere we try to understand and explain how model predictions really work detailed coverage on various aspects of model interpretation have also been coveredincluding feature importancespartial dependence plotsand prediction explanations finallywe also looked at some aspects pertaining to model deployment and the various options for deploying models this should give you good idea of how to start building and tuning models we will reinforce these concepts and methodologies in the third part of this book where we will be working on real-world case studies