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Understanding model drift, data drift, and concept drift
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Tools for real-time and batch monitoring
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Setting performance thresholds and alert systems
Exercise Files
1: Foundations of AI Maintenance
This section introduces the core principles of maintaining AI systems after deployment. You’ll learn why AI maintenance is critical for long-term model performance, how to detect data and model drift, and how to evaluate your AI system’s fairness, accuracy, and reliability over time. It sets the groundwork for understanding the challenges and responsibilities involved in keeping AI models trustworthy and effective in real-world environments.
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2.Operationalizing and Automating AI Maintenance
This section explores how to move from manual model upkeep to fully automated, scalable AI maintenance using modern tools and infrastructure. You’ll learn how to implement MLOps practices that streamline the monitoring, retraining, and redeployment of AI models. Topics include setting up CI/CD pipelines for machine learning, using cloud-native tools for observability, automating responses to model drift, and ensuring that your AI systems remain reliable, secure, and compliant with minimal manual intervention. This section equips you to maintain production-grade AI systems efficiently and at scale.
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