The subtle art of fixing silently failing ML Models
“Most businesses do not have framework in place to monitor and curb piling model issues as they scale. It’s imperative to take strategic action now or face the consequences of inaction.”
Key Takeaways
- 1
Overview of failing AI models and why they happen
- 2
How to identify silent model failures and assess their impact in the short and long term
- 3
Ways to proactively fix model failures and maintain failure resistance
Abstract
The largest issue ML teams face is due to AI models that silently fail. Silent failure occurs when model performance gradually degrades over time without showing any apparent signs of failure. These signs are therefore difficult to catch in time, usually leading to sudden or abrupt drops in performance after the gradual decline. The downstream impact is not just limited to ML or business teams as it trickles down to the end customer who faces the first-hand repercussions of incorrect predictions.
With the adoption of AI Observability tools, pioneering tech giants have managed to stay on top of silent model failures with continuous improvement. Observable AI enables a continuous collection of data from multiple touchpoints to deliver insights for improved model performance in production. It can be broken down into three high-level components: Monitoring, Explainability, and Accountability.
Join this session with Censius experts to learn how to automate model monitoring, dive deeper with root cause analysis to explain model decisions, and proactively troubleshoot your models to build reliable and compliant solutions that are resistant to silent model failures.