This endpoint triggers the execution of an AWS Lambda function, a serverless function that wraps the Python scoring code for the JMP model that predicts house prices. This application allows a user to provide input values that are a passed in an HTTP request to a REpresentational State Transfer (REST) endpoint running as an AWS API Gateway. Or the user could generate C code for applications that require the models to be embedded in a high-performance inference pipeline. Or the user could generate JavaScript code for inclusion of the model directly in a web page, mobile app or Excel UDF. Python scoring code could also be wrapped in a database User Defined Function (UDF) so that predicted values could be returned as part of SQL queries. Our use of a web application backed by a predictive model deployed as a serverless function illustrates one of many possible deployment strategies for JMP models. Movie S2 shows the final model that was deployed in AWS using the JMP-generated Python scoring code. The final web application for the Price Predictor, accessible at JMP Pro's Model Comparison enables the users to stack and easily compare these profilers (Figure 1). One of these tools is the Profiler, which enables the user to explore the model predictions across the model space. ![]() JMP Pro has many tools to facilitate fitting, comparison, and selection of predictive models. Other users might include home buyers and sellers. The agent could ask the homeowners about their home characteristics, enter these into the Price Predictor, and instantly show the homeowners a reasonable listing price for their home, based on a prediction model developed in JMP. The user might be a real estate agent going door to door to look for new inventory. The goal was to create a Price Predictor tool that could be accessed on a web application. The data include dozens of characteristics, such as listing price, number of bedrooms, number of bathrooms, square footage, and other critical factors, for a few thousand homes for sale and listed on on the date of download. The data used in this application were downloaded on. The model scoring code is deployed to a cloud provider and accessed through a web application.įor the data modelling, data were downloaded from the housing website Redfin (Redfin, 2020) for the metropolitan area of St. First, the JMP user interacts with the JMP interface to select and fit a model and subsequently to generate the Python scoring code. The JMP extensibility example in this section illustrates how to take a JMP model and deploy it into a production environment so that it can be accessed by users. 2 CASE STUDY: DELIVERING PREDICTIVE CAPABILITIES TO END USERS FROM A CLOUD-DEPLOYED MODEL The results are returned in JMP to make use of JMP's advanced graphics capabilities, including easy filtering, interactive selection, and easy hover labels or markers using images. ![]() In Section 3, we show how JMP users can access special packages in R or Python without leaving the JMP user interface and without any additional programming. In Section 2, we show a web application that allows end users to obtain house price predictions from a JMP model that has been converted to Python and deployed to a cloud provider. ![]() The following sections describe applications that we have created to show how to extend JMP with open-source languages and libraries. When the streamlined workflow is not sufficient, JMP extensibility features come into play, allowing the creation of new workflows that are augmented by the rich capabilities available in open-source programming languages, such as R (R Core Team, 2017) and Python (Van Rossum, 1995), and their large repositories of statistical methods and data manipulation processes. Or perhaps the data from a particular domain might be better suited to a modelling technique that is not yet implemented in JMP. For example, the final step of a data modelling workflow might require that the model is exported from JMP so that it can be deployed to a production environment. In some scenarios, this streamlined workflow might not be sufficient. JMP allows customers to stay focused on their analytics workflow without any programming requirements. JMP provides a comprehensive set of data access, cleaning, visualization, analysis, and modelling features. JMP, a statistical discovery application from SAS Institute (SAS Institute, n.d.), offers a highly integrated, easy-to-use user interface based on dialogues, menus, and drag-and-drop commands.
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