Cloud Decision Platform Automating Metric Feedback Loops
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Solution Overview
Problem
The development of analytic decision applications is time and resource intensive, requiring manual propagation of operational and business metrics for improvement, and existing solutions lack a unified interface for integrating decisioning technologies and managing their lifecycle effectively.
Innovation Solution
A cloud-based decision management platform that provides a unified interface for developing, configuring, deploying, and managing decision services, allowing for the use of various languages and technologies, with features like automated validation, adaptive learning, and lifecycle management, enabling cost-effective and scalable real-time decision management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If analytic decision applications are developed using conventional methods with manual processes, then developers can maintain control over the development process, but the development time and resource requirements increase significantly
Solution Approach 1:
The system enables self-service through automated propagation of operational and business metrics back to analytic scientists, eliminating manual intervention. The platform automatically captures metrics, processes them, and feeds them back for model improvement, allowing the system to serve itself without continuous human involvement in the feedback loop.
Solution Approach 2:
The system implements automated feedback mechanisms where operational and business metrics are automatically captured, processed, and propagated back to analytic scientists. This closed-loop feedback system continuously improves analytic models by delivering relevant performance data without manual intervention, significantly reducing development time while maintaining quality.
2Adaptability or versatility
If multiple languages and technologies are used for decision services, then versatility and adaptability improve, but integration complexity and deployment difficulty increase
Solution Approach 1:
The platform provides universal support for multiple programming languages and technologies through a unified cloud-based interface. It can ingest, manage, and deploy decision services written in different languages (e.g., Java, C#, Python, R) without requiring separate integration processes, making the system universally compatible while simplifying the user experience.
Solution Approach 2:
The cloud-based platform acts as an intermediary layer between diverse decision service technologies and the enterprise IT infrastructure. It provides standardized interfaces and automated integration capabilities that mediate between different programming languages and the underlying systems, reducing integration complexity while maintaining support for multiple technologies.
3Reliability
If manual propagation of metrics is used for model improvement, then data accuracy can be maintained through human review, but the time and resources required for model iteration increase
Solution Approach 1:
The system automatically captures operational and business metrics, processes them through appropriate filters and transformations, and propagates them back to analytic scientists without manual intervention. This self-service approach maintains data quality through automated validation while significantly accelerating the model iteration process.
Solution Approach 2:
The platform implements automated feedback loops that continuously capture performance metrics, validate their quality, and deliver them back to model developers. This automated feedback mechanism maintains data reliability through systematic validation while enabling rapid model iteration by eliminating manual data collection and processing steps.
4Reliability
If comprehensive lifecycle management is implemented for decision services, then system reliability and monitoring capability improve, but the complexity of the management system increases
Solution Approach 1:
The cloud-based platform provides comprehensive lifecycle management capabilities including deployment, monitoring, and maintenance of decision services through a single unified interface. It handles multiple management functions (service registration, version control, performance monitoring, automated updates) simultaneously, improving system reliability while presenting a simplified unified interface to users.
Data Source
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AI summary
A cloud-based decision management platform along with corresponding method, system, and a computer program product are disclosed. At least one component of at least one computing system is selected from a plurality of components of the computing system. The selected component is configured for execution during a runtime of the computing system. The configured component is executed during runtime. The components of the computing system are stored in a catalog module based on at least one characteristic that includes at least one of the following: analytics, decisioning, identity and access management, and optimization.