Data Fingerprinting for ML Variance Detection
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Solution Overview
Problem
Data scientists face challenges in identifying performance issues with trained machine learning models when deployed on production data, due to differences between training and production environments, and in instrumenting and logging across various libraries and platforms.
Innovation Solution
A method for fingerprinting training and production data sets by characterizing key parameters, creating data fingerprints, and comparing them to detect variances, along with a system for translating telemetry data into multiple output formats for logging across different backends.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data scientists manually analyze training and production data to identify performance issues, then model accuracy can be improved, but time consumption and complexity increase significantly
Solution Approach 1:
The system performs preliminary fingerprinting of training data during the training phase, creating a reference fingerprint before deployment. This allows for automated comparison with production data later, eliminating the need for manual analysis when performance issues arise and significantly reducing detection time.
Solution Approach 2:
The system creates a fingerprint copy of the training data characteristics and compares it against production data fingerprints. This copying approach enables automated detection of data variances without requiring direct manual examination of the actual data, thus saving time while maintaining detection accuracy.
2Adaptability or versatility
If multiple logging backends are used to capture telemetry data from different libraries and platforms, then comprehensive monitoring is achieved, but system complexity and integration effort increase
Solution Approach 1:
The fingerprinting system is designed with universal functionality that works across multiple logging backends (MLflow, TensorBoard, custom solutions) and different data types (images, audio, text). A single fingerprinting module can output to multiple backends simultaneously, eliminating the need for separate instrumentation code for each backend and reducing overall system complexity.
Data Source
AI summary
A system and method for characterizing the data used to train a model for machine learning inference. Training data and production data may both be fingerprinted, and the fingerprints may be compared to detect undesirable variances between training and production data. This may allow performance issues relating to differences in the training data set versus the production data set to be more easily identified. Parameters used for characterization can be determined based on the type of training data such as numerical data, image data, or audio data.