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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Paperback. Condizione: new. Paperback. 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9798247553465
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
Condizione: As New. Unread book in perfect condition. Codice articolo 53716757
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Da: CitiRetail, Stevenage, Regno Unito
Paperback. Condizione: new. Paperback. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Codice articolo 9798247553465
Quantità: 1 disponibili