Automated ML Pipeline Debugging via Root Cause Analysis
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Solution Overview
Problem
Machine learning pipelines are often error-prone and difficult for data scientists to debug due to numerous possible causes of errors, such as incorrect hyperparameter configurations and incorrect assumptions about input data, which can lead to crashes or unacceptable performance, and manual debugging is challenging, especially with long stack traces from deep within ML libraries.
Innovation Solution
A computer-implemented method and system that automatically generates test machine learning pipeline instances, evaluates them for failures, identifies root causes of errors, and creates a remediated pipeline to correct errors, using automated machine learning and satisfiability modulo theories (SMT) solvers to automate debugging and remedy generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual debugging is performed on machine learning pipelines, then data scientists can identify and fix errors, but the process becomes time-consuming and challenging due to numerous possible error causes and long stack traces
Solution Approach 1:
The system enables automated self-debugging of ML pipelines by generating test instances, evaluating failures, identifying root causes, and creating remediated pipelines without human intervention, allowing the system to service itself
Solution Approach 2:
The system performs preliminary actions by automatically generating multiple test pipeline instances before actual deployment, evaluating them for failures, and identifying root causes in advance, so that errors are detected and fixed before they affect production operations
2Measurement precision
If multiple test pipeline instances are generated and evaluated, then root causes of errors can be identified accurately, but the computational resources and time required increase
Solution Approach 1:
The system generates a plurality (multiple) of test pipeline instances rather than a single instance, performing excessive testing to ensure comprehensive error detection and accurate root cause identification, with the benefit that more test cases lead to better precision in identifying the actual error cause among numerous possibilities
3Productivity
If automated debugging and remediation are implemented, then the time and effort required to fix errors is reduced, but the complexity of the system increases
Solution Approach 1:
The system performs multiple functions within a single automated framework: generating test instances, evaluating failures, identifying root causes, and creating remediated pipelines. This multi-functional approach consolidates what would otherwise require separate tools and processes, managing system complexity while maintaining high productivity
Data Source
AI summary
In an approach to improve detecting and correcting errors in one or more machine learning pipelines. Embodiments comprise generating a plurality of test machine learning pipeline instances based upon a target machine learning pipeline and evaluating the plurality of test machine learning pipeline instances for failure in a task. Further, embodiments identify one or more root causes of error based upon the evaluated plurality of test machine learning pipeline instances and failure in the task, and create a remediated target machine learning pipeline based upon the identified one or more root causes of error. Additionally, embodiments output the remediated machine learning pipelines.