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Predictive Model Deployment : Predictive Model Deployment provides the option to deploy the analytical results in to every day decision making process, for automating the decision making process. The predictive models validation and deployment are time consuming activities, which takes months depending on the business scenarios. There are many challenges in deployment, as many organizations lack integrated technical infrastructure to deploy the model between different departments and business units. These challenges in deployment also include data in different data sources, requirement to integrate the model in to different applications.
Predictive Model Deployment & Monitoring
Validation of Predictive Models
Validation of predictive models are generally done for ensuring that predictors used doesn’t have legal issues, and also for the validation of distributions, analytical algorithms and pre deployment scores.
Deployment of Predictive Models
After the model is validated, the model is moved to production by implementing a scoring system where the model is applied to new data that doesn’t have a dependent variable. For the models which has impact to operational business decisions, such as an application score model or a cross sell model, the implementation system is generally an operational enterprise planning system or a transaction handling system.
Approaches in Deployment
1. Scoring the model : The model is scored and the score value is provided in to business for operational effectiveness which is used in actions and decisions. 2. Integrates with Reporting : The model is integrated with reporting in business intelligence tools and often used as a reference point for collaboration and consultation. 3. Integrates with Application : Model is integrated with applications such as call center and used in the operational business.
Monitoring of Predictive Models
The deployed predictive models are monitored for model performance. Generally the deployed models are repeatedly published in a production environment and the model performance reduces over the period of time. Organizations have process built in to systematically detect the performance reduction in the deployed models to find and obsolete models and to build new ones.
Predictive Model Markup Language
The Predictive Model Markup Language (PMML) is an XML language for statistical and data mining models which makes it easy to move models between different applications and platforms. PMML is the leading standard for predictive analytics models and supported by over 20 vendors and organizations such as IBM,SAS,SAP etc.
Functionalities in Software
1. Create, delete, merge models 2. Extract and import models in formats such as spar file and PMML format.
1.Predictive Analytics Software
RapidMiner Studio, KNIME Analytics Platform, IBM Predictive Analytics, SAP Predictive Analytics, Dataiku DSS, SAS Predictive Analytics, Oracle Data Mining ODM, Angoss Predictive Analytics, Microsoft R, Minitab, TIBCO Spotfire, AdvancedMiner, Microsoft Azure Machine Learning, STATISTICA, Anaconda, Alteryx Analytics, ABM, Google Cloud Prediction API, DataRobot, HP Haven Predictive Analytics, Analytic Solver, H2O.ai, Actian Analytics Platform, GMDH Shell, GoodData, Alpine Chorus, Portrait Predictive Analytics, FICO Model Central, GraphLab Create, Viscovery Software Suite, Information Builders WebFOCUS Platform, MATLAB, Predixion Insight, Mathematica, Rapid Insight Veera, DMWay, Lavastorm Analytics Engine, TIMi Suite, CMSR Data Miner Suite, Vanguard Business Analytics Suite, DataRPM, Feature Labs, Salford Systems SPM, Skytree, QIWare, Grapheur, Emcien, RapidMiner Server are the top predictive analytics software .
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