Distilled Machine Learning Model for Low-Latency Ranking
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Solution Overview
Problem
Complex machine learning models, such as deep neural networks, require significant computational resources for training and evaluation, making them impractical for use in computationally constrained environments like mobile devices.
Innovation Solution
The implementation of machine learning knowledge distillation techniques that transfer knowledge from a powerful teacher model to a simpler student model, allowing for accurate ranking and scoring while reducing computational requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex machine learning models like deep neural networks are used, then prediction accuracy is improved, but computational resource requirements increase
Solution Approach 1:
The patent creates a simplified copy (student model) of the complex teacher model by training it to replicate the teacher model's predictions and rankings. The student model copies the essential functionality and knowledge from the teacher model through knowledge distillation, achieving comparable performance with reduced computational complexity.
Solution Approach 2:
The patent extracts only the essential knowledge and patterns from the complex teacher model that are necessary for accurate predictions and rankings. By separating and transferring only the critical information (predictions, rankings, and relationships) to the student model, the system eliminates unnecessary computational complexity while preserving accuracy.
2Measurement precision
If complex machine learning models are deployed, then ranking accuracy is improved, but computational feasibility deteriorates
Solution Approach 1:
The student model copies the teacher model's ranking capabilities by being trained on the teacher model's predictions and outcome labels. This copying process transfers the ranking knowledge to a computationally efficient model that can be deployed in constrained environments.
Solution Approach 2:
The patent replaces the expensive, resource-intensive teacher model with a cheaper, more efficient student model for actual deployment. The student model serves as a lightweight alternative that consumes fewer computational resources while maintaining ranking accuracy.
3Reliability
If teacher models are used for training, then model performance is improved, but training time and computational cost increase
Solution Approach 1:
The teacher model is trained beforehand on comprehensive data to learn optimal predictions and rankings. Once trained, the teacher model serves as a pre-prepared knowledge source that can efficiently train the student model through distillation, avoiding the need to retrain complex models for each application.
Solution Approach 2:
Instead of training a new complex model for each task, the system copies knowledge from the pre-trained teacher model to the student model. This copying process is computationally efficient and transfers the teacher's learned performance capabilities to the student model rapidly.
4Productivity
If distilled machine learning models are deployed, then computational efficiency is improved, but model complexity reduction may affect accuracy
Solution Approach 1:
The student model carefully copies not just the teacher model's predictions but also the underlying patterns and relationships in the data. By replicating the teacher's knowledge structure, the student model maintains accuracy despite its simplified architecture.
Solution Approach 2:
The patent changes the parameters and architecture of the model from complex to simple, but compensates by adjusting the training approach (knowledge distillation). This parameter transformation allows the model to maintain predictive accuracy while achieving computational efficiency.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium for training and using distilled machine learning models. In one aspect, a method includes obtaining a first input that includes training example sets that each include one or more feature values and, for each item, an outcome label that represents whether the item had a positive outcome. A first machine learning model is trained using the first input and is configured to generate a set of scores that represents whether the item will have a positive outcome when presented in the context of the training example set and with each other item in the example set. A distilled machine learning model is trained using the set of scores for each example set. The distilled machine learning model is configured to generate a distilled score.