Items related to Capacity Engineering with Python and AI: Building Intelligen...
Capacity Engineering with Python and AI: Building Intelligent Infrastructure Management Systems Across On-Prem and Cloud (Site Reliability Engineering) - Softcover
Book 8 of 10: Site Reliability Engineering
Synopsis
In today’s hybrid, multi-cloud world, capacity planning is no longer a static spreadsheet exercise—it’s a dynamic, data-driven discipline that demands automation, intelligence, and scale. Capacity Engineering with Python and AI is a practical, hands-on guide for infrastructure engineers, SREs, and cloud architects who want to build modern capacity management systems that go beyond traditional monitoring.
This book shows you how to design and implement end-to-end capacity engineering platforms using Python, statistical forecasting, and AI-powered reasoning. You’ll learn how to collect and normalize telemetry across on-prem and cloud environments, build accurate forecasting models, detect anomalies before they become outages, and transform raw data into actionable insights.
Going further, the book introduces AI-augmented analysis using large language models to explain trends, prioritize risks, and recommend safe, auditable remediation actions—without sacrificing control or reliability.
Through real-world examples spanning AWS, Azure, Google Cloud, Kubernetes, and on-prem systems, you will build a complete pipeline: from metrics collection to automated, human-approved infrastructure changes using Terraform and Ansible.
Inside, you’ll learn how to:
- Design a scalable capacity engineering architecture across hybrid environments
- Build resilient data collection and normalization pipelines in Python
- Apply statistical and machine learning techniques for capacity forecasting
- Detect anomalies and reduce alert fatigue with intelligent systems
- Use AI as a reasoning layer to generate insights and recommendations
- Implement safe, closed-loop remediation workflows with human oversight
- Integrate capacity engineering into CI/CD, FinOps, and governance frameworks
Whether you're managing a single cluster or a global multi-cloud platform, this book equips you with the tools and patterns to turn capacity planning into a proactive, intelligent system.
"synopsis" may belong to another edition of this title.
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Capacity Engineering with Python and AI: Building Intelligent Infrastructure Management Systems Across On-Prem and Cloud (Site Reliability Engineering)
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Capacity Engineering with Python and AI
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Capacity Engineering with Python and AI (Paperback)
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Paperback. Condition: new. Paperback. In today's hybrid, multi-cloud world, capacity planning is no longer a static spreadsheet exercise-it's a dynamic, data-driven discipline that demands automation, intelligence, and scale. Capacity Engineering with Python and AI is a practical, hands-on guide for infrastructure engineers, SREs, and cloud architects who want to build modern capacity management systems that go beyond traditional monitoring. This book shows you how to design and implement end-to-end capacity engineering platforms using Python, statistical forecasting, and AI-powered reasoning. You'll learn how to collect and normalize telemetry across on-prem and cloud environments, build accurate forecasting models, detect anomalies before they become outages, and transform raw data into actionable insights. Going further, the book introduces AI-augmented analysis using large language models to explain trends, prioritize risks, and recommend safe, auditable remediation actions-without sacrificing control or reliability. Through real-world examples spanning AWS, Azure, Google Cloud, Kubernetes, and on-prem systems, you will build a complete pipeline: from metrics collection to automated, human-approved infrastructure changes using Terraform and Ansible. Inside, you'll learn how to: Design a scalable capacity engineering architecture across hybrid environmentsBuild resilient data collection and normalization pipelines in PythonApply statistical and machine learning techniques for capacity forecastingDetect anomalies and reduce alert fatigue with intelligent systemsUse AI as a reasoning layer to generate insights and recommendationsImplement safe, closed-loop remediation workflows with human oversightIntegrate capacity engineering into CI/CD, FinOps, and governance frameworksWhether you're managing a single cluster or a global multi-cloud platform, this book equips you with the tools and patterns to turn capacity planning into a proactive, intelligent system. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9798186942085
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Capacity Engineering with Python and AI
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PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000. Seller Inventory # L2-9798186942085
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Capacity Engineering with Python and AI (Paperback)
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Paperback. Condition: new. Paperback. In today's hybrid, multi-cloud world, capacity planning is no longer a static spreadsheet exercise-it's a dynamic, data-driven discipline that demands automation, intelligence, and scale. Capacity Engineering with Python and AI is a practical, hands-on guide for infrastructure engineers, SREs, and cloud architects who want to build modern capacity management systems that go beyond traditional monitoring. This book shows you how to design and implement end-to-end capacity engineering platforms using Python, statistical forecasting, and AI-powered reasoning. You'll learn how to collect and normalize telemetry across on-prem and cloud environments, build accurate forecasting models, detect anomalies before they become outages, and transform raw data into actionable insights. Going further, the book introduces AI-augmented analysis using large language models to explain trends, prioritize risks, and recommend safe, auditable remediation actions-without sacrificing control or reliability. Through real-world examples spanning AWS, Azure, Google Cloud, Kubernetes, and on-prem systems, you will build a complete pipeline: from metrics collection to automated, human-approved infrastructure changes using Terraform and Ansible. Inside, you'll learn how to: Design a scalable capacity engineering architecture across hybrid environmentsBuild resilient data collection and normalization pipelines in PythonApply statistical and machine learning techniques for capacity forecastingDetect anomalies and reduce alert fatigue with intelligent systemsUse AI as a reasoning layer to generate insights and recommendationsImplement safe, closed-loop remediation workflows with human oversightIntegrate capacity engineering into CI/CD, FinOps, and governance frameworksWhether you're managing a single cluster or a global multi-cloud platform, this book equips you with the tools and patterns to turn capacity planning into a proactive, intelligent system. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9798186942085
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