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robust-ai

Monitor production ML models and detect data or concept drift.

4|1|Updated Dec 30, 2025
Search Tags:#monitoring#drift detection#mlops#robustness#reliability#uncertainty#production ml
One-click install
npx skills add https://github.com/doanchienthangdev/omgkit --skill robust-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: robust-ai
Source: https://github.com/doanchienthangdev/omgkit/tree/main/plugin/skills/ml-systems/robust-ai
Command: npx skills add https://github.com/doanchienthangdev/omgkit --skill robust-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires prometheus_client, evidently, scipy, numpy, river, torch, airflow, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the critical challenge of ensuring Machine Learning models remain reliable and performant in production by providing tools for monitoring, detecting drift, engineering for reliability, and handling failures.

Core Features & Use Cases

  • Model Monitoring: Implement real-time monitoring using Prometheus and Grafana, or leverage Evidently AI for drift and quality reports.
  • Drift Detection: Employ statistical methods (Kolmogorov-Smirnov, Chi-square, PSI) and concept drift detectors (ADWIN, DDM) to identify changes in data distributions or relationships.
  • Uncertainty Estimation: Utilize techniques like Monte Carlo Dropout and Deep Ensembles to quantify model confidence.
  • Fallback Strategies: Design robust inference services with primary, fallback, and rule-based models, including system failure handling.
  • Automated Retraining: Set up triggers for retraining based on drift, accuracy degradation, or declining trends, integrating with orchestration tools like Airflow.
  • Use Case: A financial services company can use this skill to monitor their fraud detection model. If data drift is detected or model accuracy drops below a threshold, the skill can automatically trigger a retraining pipeline to ensure continued effectiveness.

Quick Start

Use the robust-ai skill to set up model monitoring with Prometheus and Grafana.

Frequently Asked Questions about robust-ai

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I detect data drift and concept drift in production ML models?▼

To detect data drift and concept drift in production ML models, you can use statistical tests like Kolmogorov-Smirnov, Chi-square, PSI, or concept drift detectors like ADWIN and DDM to identify changes in data distributions or relationships.

What is the best way to monitor ML model accuracy degradation in real-time?▼

The best way to monitor ML model accuracy degradation in real-time is by integrating Prometheus and Grafana for metrics tracking, or leveraging Evidently AI to generate drift and quality reports for your production environment.

How do I set up automated retraining triggers when ML model performance drops?▼

Set up automated retraining triggers by configuring thresholds for data drift, accuracy degradation, or declining trends, integrating with orchestration tools like Airflow to automatically trigger your retraining pipeline.

Can I estimate model uncertainty using Monte Carlo Dropout and Deep Ensembles?▼

Yes, you can estimate model uncertainty using Monte Carlo Dropout and Deep Ensembles to quantify model confidence, helping address silent failures and ensure robust inference in production ML environments.

Does this approach support fallback strategies for ML system failures?▼

Yes, this approach supports fallback strategies by designing robust inference services with primary, fallback, and rule-based models, including comprehensive system failure handling for production ML environments.

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