ML Model Selection via Inference Time Constraints
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Solution Overview
Problem
Users lack visibility into the inference time and associated costs of machine learning models, making it challenging to deploy them at scale due to potential high computational resource requirements.
Innovation Solution
A computer-implemented method that receives user input to evaluate machine learning models, determines acceptable architectural templates, calculates maximum neural network sizes, and provides a list of architectures and metrics that meet user-defined constraints, allowing for precise determination of inference time and cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are deployed at scale, then model performance and capability are improved, but computational resource requirements and inference time increase
Solution Approach 1:
The system performs preliminary analysis of model architectures and their computational characteristics before deployment. By evaluating and ranking architectures based on predicted inference time and resource requirements in advance, the system enables users to select appropriate models without actually running them, thus avoiding unnecessary computational resource consumption while maintaining the ability to deploy high-performance models when needed.
2Reliability
If machine learning models are deployed at scale, then model performance and capability are improved, but inference time increases
Solution Approach 1:
The system performs preliminary evaluation of model architectures to predict their inference time characteristics before actual deployment. By analyzing architectural templates and calculating expected inference durations in advance, the system provides users with information about time requirements so they can make informed decisions about model selection, avoiding deployment of models that would be too slow for their use cases.
Solution Approach 2:
The system provides feedback to users about the inferred time and resource requirements of different model architectures. By presenting this information in a user-friendly format and allowing users to filter or sort architectures based on their time and resource constraints, the system enables iterative selection processes that balance performance requirements with acceptable inference times.
3Reliability
If machine learning models are deployed at scale, then model performance is improved, but operational cost increases
Solution Approach 1:
The system performs preliminary cost estimation by analyzing model architecture characteristics before deployment. By predicting the computational resource requirements and associated costs in advance, the system enables users to evaluate the economic feasibility of deploying different models, allowing them to select architectures that provide adequate performance while staying within their operational budgets.
Solution Approach 2:
The system provides cost information feedback to users about the expected operational costs of different model architectures. By presenting cost estimates alongside performance characteristics and allowing users to filter based on cost constraints, the system enables informed decision-making that balances model performance with operational affordability.
4Ease of operation
If users treat model evaluation as a black box, then ease of operation is maintained, but visibility into inference time and cost is lost
Solution Approach 1:
The system introduces an intermediary layer between the user and the complex model evaluation process. By providing a user-friendly interface that automatically analyzes model architectures and presents summarized information about inference time and resource requirements, the system makes previously opaque technical details accessible without requiring users to understand the underlying complexity of model evaluation.
Solution Approach 2:
The system transforms complex technical parameters about model architectures into more intuitive and actionable information. By presenting model characteristics in terms of practical metrics like estimated inference time and operational cost rather than raw architectural details, the system makes the information more accessible and useful for decision-making while maintaining ease of operation.
Data Source
AI summary
An approach to identifying architectures of machine learning models meeting a user defined constraint. The approach can receive input associated with evaluating machine learning models from a user. The approach can determine acceptable architectural templates to evaluate the machine learning models based on the input and determine a list of architectures and metrics based on a calculation of maximum neural network sizes of the acceptable architectural templates not exceeding the constraint. The approach can send the list of architectures and metrics to the user for selection.