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. Mastering DevOps offers readers the knowledge and skills necessary to build, deploy, and manage DevOps practices effectively within the context of cloud engineering and data science. The book 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. The structure of the book is divided into four units, each progressively building on the concepts of the previous one. The first unit (Unit 1: Foundations of DevOps) provides the fundamental principles of DevOps, including its history, planning, and essential tools like Git. The second unit (Unit 2: Core Technologies and Architectures) introduces the core technologies and architectures that power modern DevOps, such as microservices, cloud computing, and containerization. The third unit (Unit 3: CI/CD Practices and Automation) focuses on the practical implementation of DevOps, exploring key practices like continuous integration, automation, and monitoring. Finally, the fourth unit (Unit 4: Advanced Topics and Data Science Perspective) 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 that resonate with real-world applications in cloud and data science. - Includes detailed coverage of core technologies such as cloud computing, containerization (e.g., Docker and Kubernetes), and continuous integration/delivery pipelines. - Chapters on DataOps explain how to implement DevOps principles in data pipelines and machine learning workflows, meeting the unique demands of these growing fields and empowering readers to effectively manage data and models in production.