ML Model Instrumentation Hooks for Data Collection
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Solution Overview
Problem
Conventional methods for obtaining data on the performance and operation of machine learning models require manual identification of access points and post-deployment adjustments, leading to inefficiencies and increased workload, limiting the ability to gather comprehensive insights for model improvement.
Innovation Solution
A facility that automatically inserts instrumentation hooks into machine learning models during generation, allowing for data collection without interrupting operations, and optimizes hook placement based on model analysis and user input, generating code to output data for logging and optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual identification of access points is used, then model performance data can be collected, but the process is inefficient and increases workload
Solution Approach 1:
The system automatically identifies insertion points and generates instrumentation hooks without requiring manual intervention. The machine learning framework self-configures the instrumentation by analyzing model architecture and automatically placing hooks at optimal locations for data collection, eliminating the need for manual access point identification and reducing operational workload.
Solution Approach 2:
The instrumentation hooks are inserted into the model architecture before the model is deployed for production use. This preliminary action allows the hooks to be in place and ready for data collection from the start, avoiding the need for post-deployment adjustments and ensuring efficient data gathering from the beginning.
2Loss of information
If post-deployment adjustments are made to collect data, then model insights can be obtained, but operations are interrupted and workload increases
Solution Approach 1:
The instrumentation hooks are embedded into the model architecture during the development phase, before deployment. This ensures that data collection capability is prepared in advance and no operational interruptions are needed to add instrumentation, as the hooks are already in place to capture performance data from the start of production use.
Solution Approach 2:
The instrumentation hooks are designed to collect data continuously during model operation without requiring pauses or interruptions. The hooks operate transparently in the background, allowing model inference to proceed uninterrupted while simultaneously gathering performance data for analysis and optimization.
3Loss of information
If comprehensive data collection is implemented, then model optimization insights are improved, but computational overhead increases
Solution Approach 1:
The system strategically places instrumentation hooks only at specific insertion points within the model architecture where they provide the most valuable insights for optimization. Rather than instrumenting every component uniformly, the framework identifies and instruments only the critical paths and operations that yield the most meaningful performance data, reducing unnecessary computational overhead while maintaining comprehensive optimization capabilities.
Data Source
AI summary
A facility for inserting instrumentation hooks into machine learning models is described. The facility receives an indication of a machine learning model and identifies one or more aspects of the machine leaning model. The facility receives an indication that an instrumentation hook is to be used to collect data for at least one aspect of the one or more aspects. The facility alters the machine learning model by inserting at least one instrumentation hook into the machine learning model and collects data regarding one or more aspects of the machine learning model via the instrumentation hook.