Automated Validation Engine for ETL ML Pipelines
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Solution Overview
Problem
ETL-based machine learning algorithms require labor-intensive efforts for validation against multiple test datasets, as existing methods rely on human interaction and non-independently validated ML models, leading to inefficiencies and potential invalid transformations.
Innovation Solution
The implementation of an output schema engine, validator rule definition interface, and validation engine that generates and reuses validator rules to automate the validation process, reducing user interaction and ensuring independent validation of ETL ML pipelines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual validation methods are used for ETL ML pipelines, then validation can be performed with existing tools, but labor-intensive efforts and human interaction are required
Solution Approach 1:
The system enables automated self-validation of ETL ML pipelines through the validation engine that independently executes validator rules without requiring human interaction. The engine automatically generates validation definitions, executes them against test datasets, and produces validation reports, allowing the system to validate itself rather than relying on manual human effort.
Solution Approach 2:
The patent replaces manual mechanical validation processes with an automated computational validation engine. The engine uses validator rules and validation definitions to automatically assess pipeline transformations, substituting human labor with machine-based validation that can process multiple test datasets efficiently and consistently.
2Productivity
If non-independently validated ML models are used, then model development is faster, but validation reliability is compromised
Solution Approach 1:
The validation engine acts as an intermediary between the ETL ML pipeline and the validation process. It independently executes validator rules that were not involved in model training, providing an unbiased assessment of pipeline transformations. This intermediary layer ensures that validation is performed by a separate, independent system rather than by the same components that created the model.
3Reliability
If validation is performed against multiple test datasets, then validation coverage is improved, but the complexity of the validation process increases
Solution Approach 1:
The validation engine is designed as a universal system that can handle multiple test datasets through a single unified process. The engine executes the same validator rules across different datasets, allowing comprehensive validation coverage without requiring separate validation processes for each dataset. This multi-functional approach simplifies the overall validation process while maintaining extensive coverage.
4Productivity
If automated validation is implemented, then labor-intensive efforts are reduced, but the system requires independent validation capabilities
Solution Approach 1:
The validation system is segmented into distinct modular components: the validation engine that executes validation, the validator rules that define validation logic, and the validation definitions that configure validation parameters. This segmentation allows the system to achieve automation while managing complexity through modular, independently configurable components that can be developed and maintained separately.
Data Source
AI summary
Systems and methods are provided for generating extract-transform-load (“ETL”) machine learning (“ML”) pipeline validation rules based on user-input, wherein the ETL ML pipeline validation rules may be applicable to validate an ETL ML pipeline against multiple test datasets. The ETL ML pipeline validation rules may comprise compute-type validation rules for computing expected values of data structures within a dataset output by the ETL ML pipeline. The ETL ML pipeline validation rules may comprise check-type validation rules for checking whether data structures within a dataset output by the ETL ML pipeline have intended characteristics. Where the ETL ML pipeline validation rules are applicable to validate an ETL ML pipeline against a test dataset which was not referenced to describe the ETL ML pipeline validation rules, then the ETL ML pipeline may reuse these ETL ML pipeline validation rules to validate the ETL ML pipeline without further user-input.