ML Training Data Allocation for Accuracy-Cost Tradeoffs
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Solution Overview
Problem
The challenges of high costs and time consumption in retrieving and training machine learning models using cloud computing services, along with the need to determine optimal data usage for achieving desired accuracy, are addressed by providing enhanced data allocation strategies for machine learning operations.
Innovation Solution
Determine data sampling strategies based on datasets to suggest enhanced training data allocations, optimizing time and cost by predicting additional cost and time required for accuracy improvements, and suggesting data removal to minimize expenses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all available data is used for machine learning training, then model accuracy is improved, but storage costs and processing time increase significantly
Solution Approach 1:
The patent extracts and removes redundant, duplicate, and low-value data from the training dataset. By identifying and eliminating unnecessary data points while preserving critical information, the system reduces data volume without compromising model accuracy, directly resolving the contradiction between using all data and managing data quantity.
Solution Approach 2:
The system changes parameters of the dataset by applying transformations such as normalization, aggregation, and feature selection. These parameter changes reduce the overall data volume while maintaining or enhancing the quality and predictive power of the remaining data, thereby improving the accuracy-to-volume ratio.
2Productivity
If data is processed and uploaded to cloud computing system, then machine learning operations can be performed, but storage and maintenance costs increase
Solution Approach 1:
The patent performs data preprocessing, filtering, and optimization actions before uploading data to the cloud computing system. By preparing and optimizing the dataset in advance, the system ensures that only necessary and high-value data is transferred and stored in the cloud, thereby enabling machine learning operations while minimizing cloud storage and maintenance costs.
3Measurement precision
If more training data is allocated, then model accuracy improvement is achieved, but additional costs and time requirements increase
Solution Approach 1:
The system extracts and removes redundant and duplicate data from the training set, eliminating unnecessary processing time while preserving the essential information needed for model accuracy. This extraction process directly reduces data processing time without sacrificing the accuracy improvements that would otherwise require processing more data.
4Reliability
If comprehensive data is used for training, then model robustness is improved, but data processing complexity increases
Solution Approach 1:
The system applies parameter changes through data transformations, feature engineering, and dimensionality reduction techniques. These changes simplify the data structure and processing requirements while maintaining or enhancing model robustness by preserving the most informative features and relationships in the dataset.
Data Source
AI summary
Various embodiments are provided for providing enhanced data allocation for machine learning operations in a computing environment by one or more processors in a computing system. One or more data sampling strategies may be determined based on a dataset. One or more enhanced training data allocations may be suggested for machine learning operations in a cloud computing environment based on the one or more data sampling strategies.