Predictive Model Training Platform for Cloud Resource Optimization
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Solution Overview
Problem
Existing predictive modeling technologies face challenges in efficiently training and selecting effective models, especially when dealing with large volumes of data and requiring significant computational resources and expertise, which can be a burden for client computing systems.
Innovation Solution
A system that allows predictive modeling training data to be uploaded to a server system over a network, where multiple predictive models are trained using various functions and hyper-parameter configurations, and scores are generated to select the most effective model, which is then accessed remotely by client computing systems without expending local resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple predictive models are trained with different training functions and hyper-parameter configurations to improve model selection accuracy, then the reliability of the selected model is improved, but the computational resources and time required for training increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-training multiple predictive models with different training functions and hyper-parameter configurations before the actual model selection is needed. These pre-trained models are stored and can be quickly deployed when prediction is required, eliminating the need to perform extensive training at the time of model selection.
Solution Approach 2:
The training process is segmented into multiple independent model training operations, where different predictive models are trained separately with different configurations. This allows parallel processing of training tasks and enables selective deployment of only the most suitable pre-trained model, reducing overall training time while maintaining reliability.
2Manufacturing precision
If large volumes of training data are used to improve predictive model accuracy, then the manufacturing precision of the model is improved, but the device complexity and resource requirements increase
Solution Approach 1:
The system extracts and separates the heavy computational burden of training with large volumes of data from the client-side system. Training operations are performed on remote servers or cloud infrastructure, while the client system only needs to manage smaller datasets and interact with pre-trained models, thereby reducing local device complexity while maintaining high model accuracy.
Solution Approach 2:
A remote server or cloud platform acts as an intermediary between the client system and the training data. The intermediary handles the computationally intensive tasks of processing large training datasets and training multiple predictive models, while the client system communicates with the intermediary to receive trained models or prediction results, thus avoiding direct handling of large data volumes at the client端.
3Ease of operation
If predictive modeling operations are performed locally on client computing systems, then the ease of operation is maintained, but the loss of time for model training and updating increases
Solution Approach 1:
The system performs model training operations in advance on remote servers, so that when the client system needs to use a predictive model, the training is already complete. The client system only needs to retrieve and deploy pre-trained models, maintaining operational simplicity while eliminating the time-consuming training process from the client's workflow.
Solution Approach 2:
The remote server system provides self-service capabilities by automatically training and managing multiple predictive models without requiring client intervention. The client system simply interacts with the service to request model training or deployment, and the server handles all complex operations autonomously, reducing both client operational burden and training time.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on one or more computer storage devices, for training a predictive model. In one aspect, a method includes receiving over a network predictive modeling training data from a client computing system. The training data and multiple training functions obtained from a repository of training functions are used to train multiple predictive models. A score is generated for each of the trained predictive models, where each score represents an estimation of the effectiveness of the respective trained predictive model. A first trained predictive model is selected from among the trained predictive models based on the generated scores. Access to the first trained predictive model is provided over the network.