State of the Art Model Deployment

The machine learning model lifecycle.There are countless online courses and articles about preparing the data and building models but there is much less material about model deployment..Yet, it is precisely at this stage where all the hard work of data preparation and model building starts to pay off..This is where models are used to score (or get predictions for) new cases and extract the benefits.My intent here is to fill this gap, so that you will be fully prepared to deploy your model using time tested resources..You’ll learn about an open standard and the state of the art of model deployment.Solving the Deployment ProblemModel deployment can frequently cause a number of problems due to the fact that model building and deployment is often handled by different teams..Data scientists or statisticians typically build the models, while IT employees, webmasters, or database administrators are tasked with deploying them into production..Often those teams work in different environments, possibly on different continents, using different software products, with different programming languages, operating systems or file systems..Additionally, a model cannot be deployed just by itself..The data preparation steps applied to the training data before model building needs to be applied to the new data before model scoring..Often it is not easy to preserve and re-create those steps..Without standards, developers may have to reimplement the model in a different language before it could be deployed..This, of course, is a very slow and error-prone approach.Fortunately, in the late 1990’s Professor Robert Grossman, then at University of Illinois at Chicago, organized the Data Mining Group (DMG), a group of companies working together on open standards for predictive model deployment..IBM was among the founding members of the group, and still remains a leading member.. More details

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