What is Deployment of Predictive Models ?
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.
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
Click on the button below for a review of the top predictive analytics proprietary software solutions.
R Software Environment, Dataiku, Orange Data mining, RapidMiner, Anaconda, KNIME, DMWay, HP Haven Predictive Analytics, GraphLab Create, Lavastorm Analytics Engine, Actian Vector Express, Scikit-learn, Microsoft R, H2O.ai, Weka Data Mining, Apache Spark, Octave, Tanagra, PredictionIO, Apache Mahout, LIBLINEAR, Vowpal Wabbit, NumPy, and SciPy are the Top Free Predictive Analytics Software.
More Information on Predictive Analysis Process
For more information of predictive analytics process, please review the overview of each components in the predictive analytics process: data collection (data mining), data analysis, statistical analysis, predictive modeling and predictive model deployment.
Predictive Analytics Software
You may also like to review the predictive analytics free software list :
Predictive Analytics Freeware Software
You may also like to review the predictive analytics software API :
Predictive Analytics Software API
You may also like to review the top predictive analytics proprietary software list:
Top Predictive Analytics proprietary Software