The solution helps users interpret complex black-box machine learning models by bringing out the important features which the model uses for predictions. It also identifies the features and their effect on the predictions, for each of the predictions. The solution supports 40+ tree based...
Algorithm - Fulfilled on Amazon SageMaker
Absenteeism at work forecasting generates 30 days of forward forecast of employee absenteeism using historical data. This solution helps businesses to optimize their workforce and related infrastructure in an efficient manner. It uses ensemble ML algorithms with automatic model selection...
Model Package - Fulfilled on Amazon SageMaker
Product Demand Forecasting generates 36 months of forward forecast of the demand using historical data. It uses ensemble ML algorithms with automatic model selection algorithms. This solution provides consistent and better results due to its ensemble learning approach. This solution performs...
Model Package - Fulfilled on Amazon SageMaker
Operating Expenses Forecasting generates 30 weeks of forward forecast of the operating expenses using historical data. This will help businesses predict and manage their operating expenses more effectively through better working capital management and improved planning for resource allocation. The...
Model Package - Fulfilled on Amazon SageMaker
Server Utilization Forecasting enables enterprises to optimize server allocation and utilization by generating 30 days of forward forecast of server usage. This helps enterprises to plan their server allocation strategy across the cloud and on premise scenarios using historical data. It uses...
Model Package - Fulfilled on Amazon SageMaker
The solution helps users interpret complex black-box machine learning models by bringing out the important features which the model uses for predictions. This can help the users to tweak/ modify the features to improve on models performance and help remove any biases that a particular feature can...
Algorithm - Fulfilled on Amazon SageMaker
Energy Consumption Forecasting generates 30 months of forward forecast of the consumption using historical data. It uses ensemble ML algorithms with automatic model selection algorithms. This solution provides consistent and better results due to its ensemble learning approach. This solution...
Model Package - Fulfilled on Amazon SageMaker
Gradient Boosting Machine (for Regression and Classification) is a forward learning ensemble method. The guiding heuristic is that good predictive results can be obtained through increasingly refined approximations. H2O’s GBM sequentially builds regression trees on all the features of the dataset...
Algorithm - Fulfilled on Amazon SageMaker
This is a data-driven, proactive maintenance method that is designed to analyse the condition of equipments and help predict when maintenance should be performed. Predictive maintenance of production lines is important to detect possible defects early, identifying and applying the required...
Algorithm - Fulfilled on Amazon SageMaker
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A Random Forest regression on dense data set like CSV without translating the data set into other formats like recordIO. The algorithm scales efficiently across multi-cores on a single AWS EC2 Instance out of the box.
Algorithm - Fulfilled on Amazon SageMaker
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