Monitoring & Exit
Lifecycle Management
Launch is just the beginning. We establish a comprehensive monitoring system and clear exit mechanisms to manage the entire lifecycle of your AI systems.
Continuous Monitoring
Ensure system health
Model Drift
Detect changes in data distribution (Data Drift) leading to reduced prediction accuracy.
Performance
Continuously track system latency, throughput, and resource usage to ensure service quality.
Business Metrics
Monitor AI impact on actual business KPIs to ensure investment protection.
Exit Mechanisms
When to stop or retire a model
Performance Failure
Accuracy consistently below threshold and cannot be improved via retraining.
Regulatory Change
New regulations make the current model architecture non-compliant.
Cost Prohibitive
Operational costs exceed business value generated (ROI < 1).
Obsolete Tech
Newer, more efficient technologies can completely replace the existing model.
Need MLOps Implementation?
Help you implement automated monitoring and model lifecycle management tools.
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