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Hawkeye
Overview :
HawkEye is a powerful toolkit for monitoring, observability, and machine learning workflow debugging used internally at Meta. It supports recommendation and ranking models for many of Meta's products. Over the past two years, it has achieved significant improvements in the time required to debug production issues. HawkEye includes infrastructure for continuously collecting data related to services and training models, as well as data generation and analysis components for root cause analysis. It supports guided exploration, investigation, and remediation measures for UX workflows. Through a guided exploration interface based on necessary components, HawkEye allows users to effectively investigate and resolve issues.
Target Users :
["Debugging online recommendation and ranking models","Analyzing model issues in production environments","Accelerating the identification and resolution of feature issues in model services"]
Use Cases
Using HawkEye to correlate product performance anomalies with specific model snapshots for fast problem localization
Using HawkEye's model explanation functionality to identify relevant feature subsets when model predictions exhibit偏差
Discovering label imbalance leading to poor model training performance by reviewing training data statistical information
Features
Continuously collecting data related to services and training models and data generation
Analyzing and detecting models with decreasing prediction quality
Explaining model and feature importance analysis to isolate subsets of features causing prediction changes
Conducting root cause analysis along upstream data based on statistical information and confidence measures related to feature dependencies and transformations
Comparing current model snapshots with previously stable snapshots to discover issues with training data or loss divergence
Providing visualization based on model diagrams to quickly determine the cause of bad snapshots
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