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Do we have adequate resources and MLOps practices in place to manage, monitor, and maintain our AI system?
Do we have adequate resources and MLOps practices in place to manage, monitor, and maintain our AI system?
MLOps (Machine Learning Operations) refers to the engineering and governance practices required to reliably develop, deploy, and monitor machine learning models in production. Without proper MLOps, organizations may face:
- Model Drift: Performance degradation due to changes in input data or real-world conditions.
- Lack of Traceability: Difficulty reproducing results or auditing decisions.
- Operational Failures: Models failing silently or behaving unpredictably in production.
- Compliance Risks: Inability to demonstrate accountability or meet regulatory requirements.
MLOps is especially important for high-risk AI applications under the EU AI Act, where continuous monitoring, retraining, and documentation are legal obligations.
If you answered No then you are at risk
If you are not sure, then you might be at risk too
Recommendations
- Establish clear MLOps processes including versioning, CI/CD pipelines, and model registry.
- Continuously monitor model performance, fairness, and drift.
- Ensure auditability by logging predictions, training runs, and data lineage.
- Automate testing and rollback mechanisms for safe model updates.
- Define clear responsibilities between data scientists, ML engineers, and operations staff.
- Include human-in-the-loop checks or alerts for sensitive or safety-critical applications.