This course provides advanced strategies and techniques for monitoring, managing, and maintaining machine learning models after deployment. Learn to implement robust systems for detecting drift, tracking performance degradation, automating retraining, and integrating with MLOps toolchains to ensure model reliability and effectiveness in production environments.
Prerequisites ML Deployment & Python
Level:
Advanced Monitoring Implementation
Implement sophisticated monitoring systems tailored for production machine learning models.
Drift Detection
Apply advanced techniques to detect data drift, concept drift, and model performance decay.
Automation
Design and implement automated model retraining, validation, and deployment pipelines.
MLOps Integration
Integrate monitoring solutions effectively within existing MLOps platforms and workflows.
Model Lifecycle Management
Manage model versions, implement safe deployment patterns, and establish rollback strategies.
Performance Analysis
Analyze model performance degradation using granular metrics and perform root cause analysis.
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