Monitor ML models in production for data drift and performance degradation.
npx skills add https://github.com/DTMC-marketplace/governance --skill evidently-ai
Please help me install this Agent Skill. Skill: evidently-ai Source: https://github.com/DTMC-marketplace/governance/tree/main/skills/evidently-ai Command: npx skills add https://github.com/DTMC-marketplace/governance --skill evidently-ai
This Skill addresses the critical need to monitor Machine Learning models in production, detecting performance degradation and data drift to ensure ongoing accuracy and compliance.
Use the evidently-ai skill to assess the current compliance status of the AI system against Art. 15 requirements.
High-intent search queries and answers about installing and using this skill.
You can monitor ML models in production for data drift and performance degradation by integrating the Evidently library into your deployment pipelines to generate visual reports and continuous oversight of model behavior.
Data drift occurs when input data distributions change in production. You need to track data drift when your ML models are deployed in production to ensure ongoing accuracy and mitigate performance risks.
Yes, you can use Evidently AI to assess compliance against EU AI Act Article 15 by evaluating the current state of your AI systems, implementing controls for performance risks, and documenting monitoring findings.
Yes, you need existing ML model deployment pipelines because continuous oversight requires integration with your deployed models to effectively monitor target drift, data drift, and performance degradation.
The best way to detect target drift in a fraud detection model is to continuously monitor it using the Evidently library, which tracks shifts in input data distributions and triggers alerts for investigation or retraining.
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