In Meta’s machine learning (ML) research, the development of HawkEye serves to solve the challenges of debugging at scale. This powerful toolkit tackles the complexities of monitoring, observability, and debuggability for ML-based products, which play a significant role at Meta. Identifying and resolving production issues efficiently is crucial to ensure the robustness of predictions and the overall quality of user experiences and monetization strategies.
HawkEye simplifies debugging and reduces time spent on complex production issues. It introduces a decision tree-based approach, empowering both ML experts and non-specialists to triage issues with minimal coordination and assistance.
HawkEye’s Operational Debugging Workflows
The toolkit is designed to systematically identify and address anomalies in top-line metrics. By pinpointing specific serving models, infrastructure factors, and traffic-related elements, it helps to eliminate anomalies and identify models with prediction degradation. It streamlines the mitigation process and facilitates rapid issue resolution.
Isolating Prediction Anomalies with Advanced Model Explainability
HawkEye’s unique strength lies in isolating prediction anomalies to features, leveraging advanced model explainability and feature importance algorithms. It provides real-time analyses of model inputs and outputs, enabling correlations between time-aggregated feature distributions and prediction distributions. This results in a ranked list of features responsible for prediction anomalies, enhancing the efficiency of the triage process and significantly reducing the time from issue identification to feature resolution.
In conclusion, HawkEye is a crucial solution in Meta’s quest to improve the quality of ML-based products. Its decision tree-based approach simplifies operational workflows and empowers a broader range of users to navigate and triage complex issues efficiently. With its extensibility features and community collaboration initiatives, HawkEye promises continuous improvement and adaptability to emerging challenges, playing a critical role in enhancing Meta’s debugging capabilities.