Meta's HawkEye: Revolutionizing AI Debugging for Unmatched Reliability
y Partha Kanuparthy, Animesh Dalakoti, Srikanth Kamath
Meta introduces HawkEye, a game-changing toolkit for AI debugging, offering unparalleled monitoring, observability, and debuggability for end-to-end machine learning (ML) workflows. HawkEye, developed internally, has been instrumental in achieving order-of-magnitude improvements in debugging production issues over the past two years, particularly in recommendation and ranking models across various Meta products.
Key Features and Workflows
HawkEye's Components:
- Comprehensive infrastructure for continuous data collection on serving and training models.
- Analysis components for mining root causes and guiding investigations.
- UX workflows for streamlined exploration, investigation, and mitigation actions.
AI Debugging Workflow with HawkEye:
- Alert and Anomaly Detection: Triggered by issues in key metrics or anomalies in models or features.
- Top-Line Product Issues to Model Snapshots: Pinpoint specific serving models responsible for degradation across experiments.
- Model Anomaly to Snapshot Isolation: Identify suspect model snapshots causing anomalies and assess their quality.
- Prediction Anomalies to Features: Real-time analysis of model inputs and outputs to isolate features responsible for prediction anomalies.
- Upstream Causes of Feature Issues: Trace complex data transformation pipelines to detect and correct faults before impacting live models.
- Diagnosing Model Snapshots: Compare model snapshots, run inference, and analyze model graph for issues with training data or loss divergence.
- Diagnosing Training Data Issues: Navigate from suspect snapshots to training pipelines, inspect statistical issues, and identify data drift or anomalies.
Streamlined AI Debugging with HawkEye
Decision Tree Approach:
- HawkEye implements a decision tree, streamlining the debugging process and reducing time spent on complex issues.
- Enables efficient navigation through the decision tree to identify root causes quickly.
Operational Benefits:
- HawkEye significantly reduces debugging time for complex production issues.
- Simplifies operational workflows, empowering non-experts to triage issues with minimal coordination.
Future Steps for HawkEye
- Continuous tracking of emerging root causes in production issues.
- Addition of detailed analyses to HawkEye workflows and the product surface.
- Piloting extensibility features for product teams to add generic and specialized debugging workflows.
Acknowledgments
Meta extends gratitude to the HawkEye team and partners for their contributions to the success of this initiative. Special thanks to Girish Vaitheeswaran, Atul Goyal, YJ Liu, Shiblee Sadik, Peng Sun, Adwait Tumbde, Karl Gyllstrom, Sean Lee, Dajian Li, Yu Quan, Robin Tafel, Ankit Asthana, Gautam Shanbhag, and Prabhakar Goyal.
