Manasi Vartak

For any organization whose core product or business depends on ML models (think Slack search, Twitter feed ranking, or Tesla Autopilot), ensuring that production ML models are performing with high efficacy is crucial. In fact, according to the McKinsey report on model risk, defective models have led to revenue losses of hundreds of millions of dollars in the financial sector alone. However, in spite of the significant harms of defective models, tools to detect and remedy model performance issues for production ML models are missing.

Based on our experience building ML debugging and robustness tools at MIT CSAIL and managing large-scale model inference services at Twitter, Nvidia, and now at Verta, we developed a generalized model monitoring framework that can monitor a wide variety of ML models, work unchanged in batch and real-time inference scenarios, and scale to millions of inference requests. In this talk, we focus on how this framework applies to monitoring ML inference workflows built on top of Apache Spark and Databricks. We describe how we can supplement the massively scalable data processing capabilities of these platforms with statistical processors to support the monitoring and debugging of ML models.

Learn how ML Monitoring is fundamentally different from application performance monitoring or data monitoring. Understand what model monitoring must achieve for batch and real-time model serving use cases. Then dig in with us as we focus on the batch prediction use case for model scoring and demonstrate how we can leverage the core Apache Spark engine to easily monitor model performance and identify errors in serving pipelines.