Model Vulnerabilities
Adding complexity to models can introduce vulnerabilities, especially when relying on external signals that may change unexpectedly. A lack of retraining after updates can lead to unexpected behaviors, particularly when upstream models improve or go offline. It's crucial to implement early warning systems and maintain awareness of how interconnected data sources can impact performance.In this clip
From this podcast
Gradient Dissent - A Machine Learning Podcast
D. Sculley — Technical Debt, Trade-offs, and Kaggle
Related Questions
What are some techniques for training machine learning models as discussed in the episode 689: Observing LLMs in Production to Automatically Catch Issues — with Amber Roberts and Xander Song and the clip ML Observability Insights?
How do you leverage different models in machine learning based on the episode 689: Observing LLMs in Production to Automatically Catch Issues — with Amber Roberts and Xander Song and the clip Model Maintenance Insights?