DataRPM uses patent pending Meta-Learning technology, integral component of Artificial Intelligence to automate predictions of asset failures. CPdM is a full stack platform available on cloud or on premises that connects to the data lake and then automatically runs multiple Machine Learning experiments, finds patterns & anomalies in data, identifies influencing factors & predictors, and builds an ensemble of predictive models to automate Predictive Maintenance.
Predictive Analytics Software
prevents failure | Breakdowns
Reduce Maintenance Cost
Minimize Warranty Claims & Risk
Optimize Inventory & Personnel
Contact for Pricing
Small (<50 employees), Medium (50 to 1000 employees), Enterprise (>1001 employees)
DataRPM provides an award-winning Cognitive Data Science platform on the cloud or on premises for enterprises to build data products for Predictive Analytics and Recommender Systems. Enterprises across the globe are using DataRPM to digitally transform their businesses in the core areas of Predictive Maintenance, Product Recommendations, Content Recommendations, Churn Predictions and Conversion Predictions. DataRPM prides itself in delivering the fastest and scalable automated data science platform with a natural language question answering interface which guarantees a return on investment for their customers and turns everyone in the organization into citizen data scientists. DataRPM delivers industry’s only Cognitive Predictive Maintenance (CPdM). CPdM is a full stack platform available on cloud or on premises that connects to the data lake and then automatically runs multiple Machine Learning experiments, finds patterns & anomalies in data, identifies influencing factors & predictors, and builds an ensemble of predictive models to automate Predictive Maintenance. Not only does CPdM platform delivers descriptive, predictive, prescriptive analytics through Natural Language Discovery module but also provides a closed loop system by integrating these actionable insights through APIs to ERP, CRM, and CMS systems.The Meta Learning environment runs multiple live automated ML experiments on datasets, extracts data from every experiment, trains an ensemble of models on this meta data repository, applies models to predict the best algorithms and finally builds machine-generated and human verified Machine Learning models for Predictive Maintenance. The entire workflow uses various recipes like feature engineering, segmentation, influencing factors and prediction recipes to give prescriptive recommendations.
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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.