AI Engine Selective Model Retraining for Computational Efficiency
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Solution Overview
Problem
Training complex IVR systems with multiple interconnected machine learning models is resource-intensive and inefficient, requiring significant computing resources and power consumption, and often involves unnecessary retraining of all models when only specific models need updates.
Innovation Solution
An AI system that includes a model training system and AI engine to determine which target models need retraining based on changes in training data, utilizing CPU, GPU, or other specialized hardware, and scheduling retraining only for those models, thereby reducing computational and power resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all machine learning models are retrained whenever training data changes, then model accuracy and consistency are improved, but computational load and power consumption increase significantly
Solution Approach 1:
The system segments the model retraining process by identifying and separating only those models that are actually affected by training data changes. The AI engine analyzes the relationship between training data and specific models to determine which models require retraining, rather than retraining all models uniformly. This segmentation reduces computational load while maintaining accuracy for affected models.
Solution Approach 2:
The system applies partial action by retraining only the necessary subset of models rather than all models. The AI engine determines the minimal set of models that need retraining based on the changes in training data, performing just enough retraining to maintain model accuracy without the excessive computation of retraining all models.
2Productivity
If specialized hardware is used to train machine learning models, then training speed and efficiency are improved, but cost and resource requirements increase
Solution Approach 1:
The system dynamically selects between CPU and GPU hardware based on the specific training requirements of each model. The AI engine evaluates whether each model benefits more from CPU or GPU acceleration and assigns accordingly, rather than statically allocating specialized hardware for all training tasks. This dynamic approach optimizes training speed while reducing overall hardware resource requirements.
Data Source
AI summary
A system for horizontal scaling of retraining machine learning models across operational domains is provided. The system may reduce computational overhead associated model retraining. The system may include an artificial intelligence (“AI”) engine that determines target machine learning models that need to be retrained in response to changed training data. The AI engine may assign daemons to the target models. The daemons may gather retraining requirements such as source code and training data. The daemons may schedule the target models for retraining on a CPU or a GPU based model training system.