Mastering DevOps: A Cloud Engineering and Data Science Perspective addresses the challenge of understanding and implementing DevOps in an era of rapid technological advancement where cloud-based infrastructure and data science applications have become integral to many organizations. The book covers the specific requirements of these fields, such as scalability, automation, and managing large-scale data and containerized applications. Content focuses on DevOps principles while integrating core technologies such as cloud computing, microservices, and continuous integration/continuous delivery (CI/CD). Additionally, the book provides coverage of a DevOps approach tailored to data science by covering recent advancements and explaining their relevance in a DevOps environment. Specific topics cover fundamental principles, including history, planning, and essential tools like Git, introduce the core technologies and architectures that power modern DevOps, such as microservices, cloud computing, and containerization, and focus on the practical implementation of DevOps, exploring key practices like continuous integration, automation, and monitoring. Finally, the book delves into advanced topics and future trends, such as deployment strategies and the extension of DevOps principles to data science and other narrowed-down domains. - Presents end-to-end DevOps phases with real-world applications, covering each DevOps phase, from planning to monitoring, with practical examples and scenarios- Includes detailed coverage of core technologies such as cloud computing, containerization (e.g., Docker and Kubernetes), and continuous integration/delivery pipelines- Provides chapters that explain how to implement DevOps principles in data pipelines and machine learning workflows, meeting the unique demands of these growing fields

Mastering DevOps: A Cloud Engineering and Data Science Perspective addresses the challenge of understanding and implementing DevOps in an era of rapid technological advancement where cloud-based infrastructure and data science applications have become integral to many organizations. The book covers the specific requirements of these fields, such as scalability, automation, and managing large-scale data and containerized applications. Content focuses on DevOps principles while integrating core technologies such as cloud computing, microservices, and continuous integration/continuous delivery (CI/CD). Additionally, the book provides coverage of a DevOps approach tailored to data science by covering recent advancements and explaining their relevance in a DevOps environment. Specific topics cover fundamental principles, including history, planning, and essential tools like Git, introduce the core technologies and architectures that power modern DevOps, such as microservices, cloud computing, and containerization, and focus on the practical implementation of DevOps, exploring key practices like continuous integration, automation, and monitoring. Finally, the book delves into advanced topics and future trends, such as deployment strategies and the extension of DevOps principles to data science and other narrowed-down domains. - Presents end-to-end DevOps phases with real-world applications, covering each DevOps phase, from planning to monitoring, with practical examples and scenarios- Includes detailed coverage of core technologies such as cloud computing, containerization (e.g., Docker and Kubernetes), and continuous integration/delivery pipelines- Provides chapters that explain how to implement DevOps principles in data pipelines and machine learning workflows, meeting the unique demands of these growing fields