Containerized Model Control Platform for Real-Time Probe Feedback
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Solution Overview
Problem
Current computer modeling systems face challenges in efficiently managing and tracking models, particularly in maintaining accuracy and stability, due to the complexity of monitoring and training dynamic systems like cooling or power delivery systems, where existing solutions lack effective monitoring and real-time performance evaluation.
Innovation Solution
A model control platform stack that includes a probe inventory and model inventory, allowing for the selection and deployment of probes and models to specific monitoring locations, enabling real-time data extraction and model adjustments based on inference and probe data, thereby improving model performance and stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time monitoring and model adjustment are implemented in dynamic systems, then model accuracy and stability are improved, but system complexity and computational resources increase
Solution Approach 1:
The system is divided into distinct functional layers: input layer for data reception, governance layer for model and probe management, and orchestration layer for deployment and execution. This segmentation allows complex monitoring and adjustment operations to be distributed across manageable modules, reducing overall system complexity while maintaining real-time capabilities
Solution Approach 2:
The governance layer pre-configures model inventories and probe inventories before deployment to the orchestration layer. Models are selected and probes are prepared in advance based on expected monitoring requirements, allowing the system to respond rapidly to dynamic changes without complex real-time decision-making, thus improving reliability while managing complexity
2Reliability
If continuous model evaluation and adjustment are performed, then model performance is improved, but computational resources and processing time increase
Solution Approach 1:
The system extracts only the necessary probe data required for model evaluation from the monitoring location points, rather than processing all available data. This partial action approach maintains model performance by focusing computational resources on critical metrics while reducing overall computational burden and energy consumption
Solution Approach 2:
The system implements a feedback loop where probe data is continuously extracted during model execution, used to evaluate model performance, and fed back to adjust the model. This automated feedback mechanism improves model performance through continuous evaluation while optimizing computational resource usage by only processing data when needed for evaluation
3Adaptability or versatility
If multiple probes and models are managed in inventory, then monitoring capability and adaptability are improved, but system complexity and management overhead increase
Solution Approach 1:
The governance layer maintains universal inventories that can store multiple types of models and probes with different functions. These inventories serve multiple purposes: storing configured models for deployment, cataloging available probes for selection, and providing a centralized repository for model-probe associations. This multi-functionality improves monitoring capability across different dynamic systems while reducing management overhead through a single unified management interface
Data Source
AI summary
The present disclosure relates to a system and a method for model control platform stack. The method includes, at an input layer of a model control platform stack, receiving input data. At a governance layer of the model control platform stack, the method includes maintaining a probe and model inventories; selecting a model, a monitoring location point, and a probe; and deploying, based on the selections of the probe and the model, a container to an orchestration layer of the model control platform stack. At the orchestration layer of the model control platform stack, the method includes accessing the container; using the container to deploy the probe and the model; scheduling an execution of the model to determine inference associated with the input data; during the execution, extracting probe data, using the probe, from the monitoring location point; and adjusting, based on the probe data and the inference, the model.