Bootstrap Framework for Cross-Company ML Model Generalization
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Solution Overview
Problem
In warehouse logistics, training and testing machine-learning models for forklifts and Autonomous Mobile Robots (AMRs) to optimize operations in new warehouses is inefficient, requiring a large dataset accumulation before effective models can be deployed, leading to potentially less efficient operations during this period.
Innovation Solution
A central node provides compute and storage resources to train and test ML models, leveraging datasets from multiple warehouses to automatically select the best initial ML model for new customers, using a Deep Bootstrap Framework to estimate generalization error and minimize the difference between pre-trained and new model performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ML models are trained using traditional methods requiring large dataset accumulation, then model accuracy and reliability are improved, but deployment time and operational efficiency are worsened
Solution Approach 1:
The system performs preliminary training of multiple ML models across diverse warehouse scenarios before they are needed. Pre-trained models are stored in a model repository, so when a new warehouse joins, an appropriate pre-trained model can be immediately deployed without waiting for dataset accumulation and training from scratch.
Solution Approach 2:
Instead of training models independently for each new warehouse, the system creates and stores multiple pre-trained model copies in a model repository. These model copies can be quickly selected and deployed to new warehouses, avoiding the time-consuming process of training new models from scratch while maintaining model effectiveness.
2Adaptability or versatility
If ML models are trained from scratch for each new warehouse, then model adaptability to specific warehouse operations is improved, but resource consumption and training time are worsened
Solution Approach 1:
The system trains and stores multiple pre-trained ML models that cover diverse warehouse scenarios, operational parameters, and equipment types. This universal model repository serves multiple warehouses with different characteristics, allowing the system to provide adaptable solutions without retraining for each individual warehouse.
Solution Approach 2:
The system varies training parameters across different model training processes, including dataset composition, operational parameters, equipment types, and warehouse configurations. By training models with different parameter configurations and storing them in the repository, the system can select the most appropriate pre-trained model for each new warehouse's specific characteristics.
3Reliability
If comprehensive datasets are collected from multiple warehouses before model deployment, then model generalization capability is improved, but data collection time and operational disruption are worsened
Solution Approach 1:
The system performs the data collection and model training actions in advance, before new warehouses need to deploy ML models. Comprehensive datasets are collected from multiple existing warehouses, and models are pre-trained with this diverse data, so when new warehouses join, generalization-capable models are already available without requiring new data collection or training.
Solution Approach 2:
The system merges datasets from multiple different warehouses, each with unique operational parameters and characteristics, to create comprehensive training datasets. By combining data across diverse sources, the pre-trained models develop strong generalization capabilities that work effectively across different warehouse environments without requiring each warehouse to contribute data individually.
Data Source
AI summary
One example method includes determining a first test error for machine-learning (ML) models when the ML models are trained using a first dataset obtained from various near-edge nodes. A second test error is determined for the ML models when the ML models are trained using a second dataset obtained from a new near-edge node. A bootstrap error for each of the ML models is determined based on the first and second test errors. A convergence value for each of the ML models is determined when the ML models are trained using the first dataset. One of the plurality of ML models is automatically selected to deploy at the new near-edge node based on the bootstrap error and the convergence value for each of the ML models.