AlerTiger: Revolutionizing AI Model Health Monitoring at LinkedIn
From Data to Insights : Ensuring the Success of AI Models in Data-driven Companies
In today’s data-driven world, artificial intelligence (AI) models have become indispensable for developing innovative products and intelligent business solutions. Companies like LinkedIn heavily rely on AI models to drive their growth and success. However, the health and performance of these models are critical factors that can determine the overall business success. To address these concerns, LinkedIn has developed a cutting-edge solution called AlerTiger, a deep-learning-based MLOps model monitoring system that revolutionizes AI model health monitoring.
The Challenges of Model Monitoring in Industries
Monitoring and alerting for model health pose unique challenges in industries. These challenges include a lack of clear metrics to define model health, label sparsity, and the fast iteration of models and features, resulting in short-lived entities. Additionally, as a product, model monitoring systems need to meet requirements for scalability, generalizability, and explainability. Overcoming these challenges is crucial for ensuring the seamless performance and reliability of AI models.
Introducing AlerTiger: The Ultimate Solution for AI Model Health Monitoring
To tackle the complex challenges of model health monitoring, LinkedIn has introduced AlerTiger, a deep-learning-based MLOps model monitoring system that empowers AI teams across the company to monitor their AI models’ health with exceptional precision. AlerTiger is a comprehensive, end-to-end solution that employs advanced techniques to detect anomalies in the models’ input features and output scores over time.
The Four Major Components of AlerTiger
AlerTiger comprises four major components that work seamlessly together to provide comprehensive and actionable insights into AI model health:
1. Model Health Statistics Generation:
During the model scoring process, AlerTiger’s serving pipeline captures and stores the model’s input features and output scores to the Hadoop Distributed File System (HDFS) via Kafka. Subsequently, a daily offline Spark job aggregates this data for each model and computes the daily healthiness statistics. These statistics serve as critical indicators of AI model health and lay the foundation for anomaly detection.
2. Deep Learning Anomaly Detection:
Leveraging the power of deep learning, AlerTiger employs a sophisticated anomaly detection algorithm to analyze the healthiness statistics of a model. This algorithm identifies anomalies in both the input features and output scores, helping AI teams pinpoint areas of concern that may impact the model’s performance and business outcomes.
3. Anomaly Post Processing:
AlerTiger applies advanced anomaly filtering and grouping logic to the univariate time series anomalies detected at the model level. By aggregating and filtering anomalies, the system achieves a high level of precision in detecting genuine issues. This ensures that AI teams receive meaningful and actionable alerts that require their attention and intervention.
4. Alerting and Visualization:
AlerTiger takes model health monitoring to the next level by providing AI model owners with holistic reports that compile all the crucial information about the anomalies detected. These reports include feature importance, example abnormal feature values, feature distribution, model traffic, anomaly patterns, and more. By visualizing these insights through plots and descriptive information, AlerTiger enables AI engineers to quickly understand the issues and take appropriate actions to rectify them.
Real-world Impact: Success Stories of AlerTiger at LinkedIn
The deployment of AlerTiger at LinkedIn’s ProML platform has yielded remarkable results and significant business gains. Let’s explore two success stories that demonstrate the power of AlerTiger in detecting and resolving critical model issues:
Success Story 1: Unveiling Feature Distribution Changes
AlerTiger detected an abnormal change in the distribution of nine features in a production model. Timely alerts were sent to the model owners, who investigated and discovered that a UI migration had altered the tracking data schema. This schema change resulted in missing observed features for certain members, adversely affecting the model’s performance. With this valuable insight, the AI team retrained the model with the new schema, leading to a substantial improvement in business metrics.
Success Story 2: Ensuring Consistency in Model Rollout
During the ramp-up of a new model, AlerTiger detected anomalies in several features that exhibited the same values for all members. These anomalies caused inconsistent online performance, hindering further model rollout. The model owners investigated the issue and identified default values in the online system as the culprit. After rectifying this issue, the new model was successfully ramped, and the anticipated business gains were realized.
Advancing AI Model Health Monitoring with AlerTiger
AlerTiger stands as a game-changing solution in the field of AI model health monitoring. Its deep-learning-based approach, combined with anomaly detection, advanced filtering, and comprehensive reporting, empowers AI teams to proactively monitor and maintain the health and performance of their models. With AlerTiger, LinkedIn has achieved significant improvements in AI model outcomes, ensuring enhanced business success and driving innovation forward. Go ahead and adapt it for your MLOps within the organization.Â
References:
Research Paper: AlerTiger: Deep Learning for AI Model Health Monitoring at LinkedIn
GitHub Code: AlerTiger GitHub Repository