Implicit Data Dependency Capture for ML Model Provenance
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Solution Overview
Problem
Existing data provenance solutions for machine learning models require explicit dependency tracking, which is cumbersome, error-prone, and inflexible, making it difficult to obtain and utilize the data history associated with model creation.
Innovation Solution
An implicit dependency tracking tool that intercepts user actions during model training or inference to generate a log of data access without altering source code, capturing file reads, git repository information, and cloud storage interactions, enabling robust data and model provenance tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If explicit dependency tracking is used to capture data provenance, then data origin information can be obtained, but the system becomes cumbersome, error-prone, and inflexible
Solution Approach 1:
The system automatically tracks data dependencies and generates provenance information without requiring user intervention. The dependency tracking mechanism operates autonomously, capturing data flows and transformations throughout the ML pipeline without users needing to manually declare dependencies or configure tracking parameters.
Solution Approach 2:
The patent introduces an intermediary dependency tracking mechanism that sits between data sources and ML processing components. This intermediary automatically captures data access patterns, tracks transformations, and builds provenance graphs without requiring changes to existing ML code or user workflows.
2Loss of information
If explicit dependency tracking tools are implemented, then data history can be captured, but the implementation becomes fragile and complex
Solution Approach 1:
The dependency tracking system is self-healing and automatically recovers from errors. When tracking failures occur, the system detects them and continues operation with available information, ensuring provenance data is captured despite individual component failures or unexpected runtime conditions.
Solution Approach 2:
The patent implements redundant tracking mechanisms and fallback procedures to cushion against potential failures. Multiple data capture methods are prepared in advance, and if one method fails, another takes over, ensuring continuous provenance tracking without data loss.
3Loss of information
If users manually define data dependencies, then provenance information can be obtained, but the process is time-consuming and error-prone
Solution Approach 1:
The system performs preliminary automatic dependency analysis before ML training begins. It pre-tracks all data accesses, transformations, and dependencies throughout the pipeline, building a complete provenance graph in advance so that when training occurs, the provenance information is already captured without requiring manual configuration during the actual ML workflow.
Solution Approach 2:
The dependency tracking operates autonomously throughout the ML pipeline, automatically detecting data flows, transformations, and dependencies without user intervention. The system self-configures tracking parameters, self-monitors data access patterns, and self-generates provenance reports.
4Loss of information
If centralization on one tool is required for dependency tracking, then provenance can be obtained, but flexibility and adaptability are reduced
Solution Approach 1:
The patent creates a universal dependency tracking framework that can monitor multiple data sources, storage systems, and ML frameworks simultaneously. The same tracking mechanism works across different data formats, cloud storage providers, and ML libraries, eliminating the need to specialize in one tool and enabling flexible, multi-platform provenance tracking.
Data Source
AI summary
The disclosure is directed to systems, methods, and computer-readable media for observability and data audit using implicit data dependency capture. Data dependency information can be intercepted, for example, as a user trains or otherwise interacts with a machine learning (ML) model. Data dependency information can include information regarding files, data sources, inputs, outputs, storage buckets, storage directories, and/or other pertinent information. A log of the data dependency information can be reviewed to determine ML model provenance.