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Python Handbook for AIOps / MLOps is a practical, engineer-focused guide that equips AI/ML Site Reliability Engineers (SREs), MLOps engineers, and Data Scientists with the Python skills required to build, operate, and scale reliable AI systems in production.
Unlike generic Python or ML books, this handbook focuses on operational Python-the patterns, libraries, and practices used to automate pipelines, monitor models, detect anomalies, manage data and feature stores, and ensure reliability across modern cloud-native AI platforms.
The book bridges the gap between data science experimentation and production-grade AI operations, emphasizing real-world use cases such as incident prediction, model drift detection, automated retraining, observability, and infrastructure-aware ML workflows.
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