AI Model Lifecycle Management via Multi-Parameter Evaluation
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Solution Overview
Problem
Artificial intelligence (AI) and machine learning models often fail to transition from the prototyping phase to production due to lack of effective lifecycle management, with existing solutions not adequately considering multiple parameters beyond model accuracy for selection and not utilizing available computing resources efficiently.
Innovation Solution
A system and method for managing the lifecycle of AI and machine learning models, including model selection based on multiple parameters, retraining non-selected models with new data, and recalibrating training routines with available computing resources, while allowing users to set criteria such as cost, time, and accuracy to fit business needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If model selection is based only on model accuracy, then the selection process is simple, but models fail to transition to production due to lack of multi-parameter evaluation
Solution Approach 1:
The patent transforms the model selection process from single-parameter (accuracy-only) evaluation to multi-parameter evaluation by introducing additional parameters such as computational resources, deployment environment compatibility, and business criteria. This allows models to be assessed comprehensively across multiple dimensions simultaneously, enabling better production readiness determination without excessive complexity.
Solution Approach 2:
The system creates a universal model selection framework that can evaluate models across multiple parameters and criteria simultaneously. The platform serves multiple functions including model training, evaluation, selection, and deployment management within a single integrated system, making the selection process adaptable to various production scenarios while maintaining manageable complexity through standardized procedures.
2Productivity
If computing resources are allocated to train multiple models, then more models can be evaluated for production, but resource utilization becomes inefficient
Solution Approach 1:
The system dynamically allocates computing resources based on real-time model training progress, performance metrics, and production readiness assessment. Resources are not statically assigned but flexibly adjusted during the training and evaluation process, allowing the system to concentrate computational power on promising models while reducing or stopping resources for models that are unlikely to succeed, thereby improving both throughput and efficiency.
Solution Approach 2:
The patent implements a staged evaluation approach where models receive computing resources in phases rather than all at once. Initial resources are allocated for prototype training, then additional resources are provided only to models that demonstrate sufficient promise. This partial action approach allows the system to evaluate multiple models at different resource levels simultaneously, increasing overall productivity while maintaining efficient resource utilization by not over-investing in models that will not make it to production.
3Quantity of substance
If models are archived after prototyping without retraining, then storage space is saved, but potential production-ready models are lost
Solution Approach 1:
The system performs preliminary assessment of models during the prototyping phase using multiple parameters including accuracy, resource requirements, and business criteria. Models that show promise in this preliminary evaluation are identified for potential retraining and production deployment before being archived. This preliminary action ensures that potentially production-ready models are not lost when storage space becomes constrained, as the system has already identified which models warrant retention or retraining based on comprehensive evaluation.
Solution Approach 2:
The patent implements a selective archiving and retraining mechanism where models are not simply discarded after prototyping. Instead, the system archives models with the option to recover and retrain them based on evolving business needs, new data availability, or changes in production requirements. This allows the system to save storage space by archiving models while maintaining the capability to recover and retrain promising models when conditions change, thus balancing storage efficiency with model production readiness.
Data Source
AI summary
A model lifecycle management method includes: executing a model initial development phase based on at least a first criteria, a second criteria, and a third criteria to obtain a set of production ready models; executing, using the set of production ready models, a model production phase based on at least a fourth criteria, a fifth criteria, and a sixth criteria to obtain; and executing, after executing the model production phase, using the set of models to be updated, a model update phase based on at least a seventh criteria on at least one model in the model production phase.