On-Demand Cloud ML Model Instantiation with User Datasets
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Solution Overview
Problem
Existing machine-learning model training systems lack flexibility and efficiency, as they often automatically select models and restrict user choice, require significant re-training for specific datasets, and limit control over training algorithms, leading to inflexible and inaccurate model implementation.
Innovation Solution
The on-demand model instantiation system allows users to select machine-learning models and datasets, utilizing a scheduler to monitor cloud-based computing devices for instantiating and training models, providing interactive dataset visualizations and control over training processes, thereby improving flexibility, efficiency, and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If existing systems automatically select machine-learning models, then model selection is simplified, but user choice and flexibility are limited
Solution Approach 1:
The system enables users to independently select and configure machine-learning models according to their specific needs. The interface allows users to browse available models, select appropriate ones, and customize training parameters without requiring automated selection, thereby providing both ease of operation and user flexibility simultaneously.
2Manufacturing precision
If existing systems require significant re-training for specific datasets, then model accuracy for specific tasks is improved, but training time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and preparing datasets before actual model training begins. This includes data validation, formatting, and organization steps that are completed in advance, reducing the time required during the actual training process while maintaining model accuracy for specific datasets.
Solution Approach 2:
The system optimizes training parameters dynamically based on the specific dataset characteristics and model requirements. By adjusting learning rates, batch sizes, and other hyperparameters according to the data being trained on, the system achieves high accuracy while minimizing training time and computational resource consumption.
3Device complexity
If existing systems restrict use to a specific service, then system management is simplified, but flexibility and vendor agnosticism are reduced
Solution Approach 1:
The system is designed with universal compatibility across multiple cloud service providers. It implements a standardized interface layer that can work with different cloud vendors (AWS, Azure, GCP, etc.), allowing the same model training pipeline to operate on any cloud platform without modification. This maintains simple system management while providing extensive flexibility and vendor independence.
4Device complexity
If existing systems limit control over training algorithms, then system simplicity is maintained, but user control and customization are reduced
Solution Approach 1:
The system segments the model training process into distinct, independently controllable modules. Users can select and configure specific training algorithms, loss functions, optimization methods, and evaluation metrics separately. This modular approach maintains overall system simplicity while providing granular user control over each aspect of the training process.
Data Source
AI summary
This disclosure describes methods, non-transitory computer readable storage media, and systems that provide a platform for on-demand selection of machine-learning models and on-demand learning of parameters for the selected machine-learning models via cloud-based systems. For instance, the disclosed system receives a request indicating a selection of a machine-learning model to perform a machine-learning task (e.g., a natural language task) utilizing a specific dataset (e.g., a user-defined dataset). The disclosed system utilizes a scheduler to monitor available computing devices on cloud-based storage systems for instantiating the selected machine-learning model. Using the indicated dataset at a determined cloud-based computing device, the disclosed system automatically trains the machine-learning model. In additional embodiments, the disclosed system generates a dataset visualization, such as an interactive confusion matrix, for interactively viewing and selecting data generated by the machine-learning model.