Model Similarity Scoring for Faster Deployment Optimization
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Solution Overview
Problem
Existing methods for optimizing trained models before deployment are often manual, inaccurate, and resource-intensive, and lack automation for identifying operations that can be removed or consolidated without impacting model accuracy.
Innovation Solution
Automate the optimization process by calculating a model summary score based on categorized operations, identifying similar models with stored optimization operations, and applying these operations to update and deploy the model in a computing environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual optimization methods are used to optimize models before deployment, then model accuracy can be maintained, but the process becomes resource-intensive and time-consuming
Solution Approach 1:
The system performs preliminary actions by pre-optimizing similar models and storing their optimization operations in a database. When a new model needs optimization, the system retrieves pre-computed optimization operations from the database based on model similarity, avoiding the need to perform optimization from scratch and significantly reducing optimization time while maintaining accuracy.
Solution Approach 2:
The system creates copies of optimization operations from similar models. By identifying models with similar characteristics and copying their optimization operations, the system can quickly apply proven optimization strategies to new models without manual intervention, reducing time loss while preserving model accuracy through transfer of effective optimization patterns.
2Reliability
If manual optimization methods are used to optimize models before deployment, then model accuracy can be maintained, but the process becomes complex and lacks automation
Solution Approach 1:
The system enables self-service automation by automatically computing model summary scores, identifying similar models in the database, selecting appropriate optimization operations, and applying them to update the target model. This automated pipeline eliminates manual optimization steps while maintaining accuracy through systematic retrieval and application of proven optimization operations from similar models.
Solution Approach 2:
The system implements feedback mechanisms by computing model summary scores that characterize model properties, using these scores to retrieve optimization operations from the database, applying them to update the model, and storing the updated model and its optimization operations back in the database. This closed-loop feedback system enables continuous improvement and automation while preserving model accuracy through iterative optimization based on similarity metrics.
3Productivity
If comprehensive model optimization is performed manually, then deployment efficiency can be improved, but computing resources are excessively consumed
Solution Approach 1:
The system applies partial optimization by selectively retrieving and applying only the most relevant optimization operations from similar models based on model summary score similarity. Instead of performing comprehensive manual optimization that consumes excessive computing resources, the system applies a targeted subset of optimization operations that are most likely to improve deployment efficiency, reducing resource consumption while maintaining productivity gains.
4Use of energy by moving object
If model optimization is automated using similarity measurements, then resource consumption is reduced, but the accuracy of optimization may be impacted
Solution Approach 1:
The system replaces manual mechanical optimization processes with automated computational methods based on model similarity measurements. By substituting human expert judgment with automated algorithms that compute model summary scores and retrieve optimization operations from the database, the system reduces computing resource consumption while maintaining accuracy through systematic, data-driven selection of optimization operations from proven similar models.
Data Source
AI summary
Methods and systems for optimizing models for deployment based on similar models. In one embodiment, a method is provided that includes receiving a model for deployment in a computing environment and extracting a plurality of operations from the model. The operations may be categorized based on predefined operation categories to form categorized operations and a model summary score may be calculated based on the categorized operations. A similar model may be identified with a similar model summary score to the model similarity score of the model. The model may be updated based on an optimization operation performed on the similar model and the model may be deployed within the computing environment.