AI Pipeline Data Validation Using Training Data Models
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Solution Overview
Problem
Existing AI pipelines face inefficiencies due to the processing of invalid or incomplete input data, leading to erroneous outputs and unnecessary resource consumption.
Innovation Solution
An AI pipeline framework that includes real-time data validation mechanisms to detect and correct invalid input data during runtime, pausing processing when anomalies are found and resuming from the stopped location with corrected data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the AI pipeline processes all input data without validation, then productivity is maintained, but reliability deteriorates due to erroneous outputs from invalid data
Solution Approach 1:
The system performs data validation before processing to identify and flag invalid data points. By validating input data against the training data model in advance, the system prevents erroneous outputs without requiring complete reprocessing, thus maintaining reliability while minimizing productivity impact.
Solution Approach 2:
The validation mechanism extracts and identifies only the invalid portions of input data, separating them from valid data. This allows the system to process valid data normally while flagging or excluding invalid data points, maintaining overall productivity while improving output reliability.
2Reliability
If the AI pipeline validates input data against training content, then reliability is improved, but device complexity increases due to additional validation mechanisms
Solution Approach 1:
The validation mechanism leverages the existing training data model, which already contains information about valid data patterns. By reusing this existing model for validation purposes, the system adds validation functionality without requiring separate complex validation rules or additional data structures.
Solution Approach 2:
The system uses its own training data model to perform validation, making the validation process self-contained within the existing AI pipeline infrastructure. This eliminates the need for external validation systems or additional complexity while ensuring data reliability.
3Reliability
If the AI pipeline stops execution on invalid data, then reliability is improved, but loss of time increases due to paused processing
Solution Approach 1:
The system extracts and flags only the invalid data points while allowing valid data processing to continue uninterrupted. This selective approach maintains processing accuracy for valid data while minimizing time loss by avoiding complete pipeline pauses.
Solution Approach 2:
The validation mechanism enables the pipeline to quickly identify and skip invalid data points rather than stopping entirely. This allows the system to rush through the validation check and continue processing valid data, maintaining both accuracy and efficiency.
Data Source
AI summary
An example operation may include one or more of storing a model of training data of an artificial intelligence (AI) model in a storage of a software application, receiving a request to execute an AI pipeline including the AI model on input data via the software application, determining that the input data is not valid data based on a comparison of the input data to the model of the training data, retrieving additional input data from the storage of the software application, determining that the additional input data is valid data based on a comparison of the additional input data to the model of the training data, and executing the AI pipeline including the AI model on the additional input data to generate a predictive output.