Automated AI Model Validation via Dataset Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The deployment of artificial intelligence models is often delayed due to resource constraints, as the number of data scientists far exceeds the number of model validators, and model validation requires significant time, especially since validation data must be different from the training data and is often unavailable to data scientists.
Innovation Solution
A system, method, and computer program product for automating model validation by segmenting an original dataset into a model training dataset and a holdout dataset, generating a model with the training dataset, and validating it using the holdout dataset, thereby reducing dependency on model validators and streamlining the validation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual model validation is performed by model validators, then validation quality is maintained, but deployment time increases due to resource constraints
Solution Approach 1:
The dataset is automatically segmented into training dataset and validation dataset, with the validation dataset being further divided into holdout datasets. This segmentation enables automated validation processes while maintaining data quality standards, reducing dependency on manual validator resources and accelerating deployment timelines.
Solution Approach 2:
The system performs self-validation by automatically using segmented validation datasets to evaluate model performance. The automated validation process eliminates the need for extensive manual validation by model validators, significantly reducing deployment time while maintaining validation quality through systematic holdout dataset evaluation.
2Productivity
If more model validators are hired to reduce deployment delays, then validation capacity increases, but resource costs increase
Solution Approach 1:
The validation system performs self-validation using automatically segmented holdout datasets, eliminating the need to hire additional model validators. The automated process maintains validation capacity while avoiding increased resource costs by leveraging computational methods rather than human validator expansion.
Solution Approach 2:
Manual validation processes are replaced with automated computational validation using holdout datasets. This substitution of mechanical human validation with automated systems increases validation capacity without proportionally increasing validator resource requirements.
3Productivity
If validation data is made available to data scientists for model training, then model development efficiency improves, but validation reliability decreases
Solution Approach 1:
The validation dataset is segmented into separate holdout datasets that are strictly isolated from the training process. This segmentation ensures that data scientists cannot access validation data during model development, maintaining validation reliability while still enabling efficient model training on the training dataset.
Solution Approach 2:
The holdout datasets are extracted and separated from the overall dataset, making them unavailable to data scientists during model training. This extraction maintains the integrity of validation data while allowing unrestricted access to training data, balancing development efficiency with validation reliability.
Data Source
AI summary
A processor may receive an original dataset. The processor may segment, automatically, the original dataset into a plurality of data groups. The plurality of data groups may include a model training dataset and a holdout dataset. The processor may generate a model with the model training dataset. The processor may validate the model with the holdout dataset.