the results depicted in figure - once again show us the features that were primarily responsible for the model to predict the data point as label having malignant cancer the feature worst area was again the most influential one and you can notice the stark difference in its value as compared to the previous data point hopefully this should give you some insight into how model interpretation works point to remember here is that we are just getting started with model interpretation based on the recent interest since but it is going to be good and worthwhile journey toward making models easy to understand for anyonem odel deployment the tough part of the whole modeling process is mostly the iterative process of feature engineeringmodel buildingtuningand evaluation once we are done with this iterative process of model developmentwe can breathe sigh of relief--but not for longthe final piece of the machine learning modeling puzzle is that of deploying the model in production so that we actually start using it in this sectionyou learn of the various ways you can deploy your models in action and the necessary dependencies that must be taken care of in this process model persistence model persistence is the simplest way of deploying model in this scheme of things we will persist our final model on permanent media like our hard drive and use this persisted version for making predictions in the future this simple scheme is good way to deploy models with minimal effort model development is generally done on static data source but once deployedtypically the model is used on constant stream of data either in realtime\near-realtime or in batches for exampleconsider bank fraud detection modelat the time of model developmentwe will have data collected over some historical time span we will use this data for the model development process and come up with model with good performancei model that is very good at flagging potential fraud transactions the model then needs to be deployed over all of the future transactions that the bank (or any other financial entityconducts it means that for all the transactionswe need to extract the data required for our model and feed that data to our model the model prediction is attached to the transaction and on the basis of it the transaction is flagged as fraud transaction or clean transaction in the simplest scheme of thingswe can write standalone python script that is given the new data as soon as it arrives it performs the necessary data transformations on the raw data and then reads our model from the permanent data store once we have the data and the model we can make prediction and this prediction communication can be integrated with the required operations these required operations are often tied to the business needs of the model in our case of tagging fraudulent transactionsit can involve notifying the fraud department or simply denying the transaction most of the steps involved in this process like data acquisition\retrievalextractionfeature engineering and actions to be taken upon prediction are related to the software or data engineering process and require custom software development and tinkering with data engineering processes like etl (extract-transform-loadfor persisting our model to diskwe can leverage libraries like pickle or joblibwhich is also available with scikit-learn this allows us to deploy and use the model in the futurewithout having to retrain it each time we want to use it from sklearn externals import joblib joblib dump(logistic'lr_model pkl'