As software development continues to evolve with the aim of faster development cycles, organizations are increasingly turning to model-driven DevOps. Model-driven DevOps enables developers to create and manage applications faster and more efficiently by taking advantage of automation tools and techniques. In this introduction to model-driven DevOps, readers will explore how leveraging automation can help organizations achieve faster development cycles and improved software quality. The book also discusses the different components of model-driven DevOps and how they can be used to streamline the development process.

The book is structured in six chapters, with each chapter being connected to the previous one. The authors guide readers on a fascinating journey through DevOps infrastructure(s), and how the book is structured provides very deep coverage of the most important concepts and mechanisms.

Chapter 1 explains the necessity for a shift in the legacy operating model for network infrastructure, provides a quick introduction to how DevOps may potentially solve many of the issues with the legacy model, and investigates the reasons DevOps is not generally used for on-premises information technology (IT) infrastructure.

Chapter 2 introduces ideas like source of truth and data models, defines the aim of business transformation, and starts talking about the high-level framework for model-driven DevOps.

Chapter 3 discusses the main reason for which we should abandon the box-by-box command-line interface (CLI) administration methodology if network infrastructure is to support business change. It also analyzes how we may use and scale application programming interfaces (APIs) and argues that they are the new CLI.

Because APIs let you interact programmatically with network infrastructure, you don't need to be a programmer to use them. Chapter 4 examines the use of ideas like data models, source of truth, configuration management tools, and templating tools to describe the network infrastructure as code. With these technologies working together, users can manage their network infrastructures much like they would in the cloud and enable infrastructure as code.

Infrastructure as code is very powerful, but it also comes with a lot of risk if used carelessly, like many other powerful things. Chapters 5 and 6 examine the ideas behind version control systems, data validation tools, simulation platforms, and continuous integration/continuous delivery (CI/CD). Together, these tools make it possible to automate compliance and security while safely utilizing infrastructure as code at scale.

This book is largely concerned with the technical details of putting model-driven DevOps into practice. However, the journey is not limited to the technical difficulties. It is impossible to overestimate the significance of the human aspects involved in the dissolution of organizational silos, culture transformation, and the skills gap. Chapter 7, “Human Factors,” explains why it is important to consider both the human and technological aspects of implementing DevOps.

The authors manage to bring new insights in terms of the benefits of model-driven DevOps, how model-driven DevOps can improve software quality, and strategies for the successful implementation of model-driven DevOps. They point out that while using model-driven DevOps can bring many advantages, there are a few specific benefits worth highlighting:

• One of the main advantages of model-driven DevOps is that it helps shorten development cycles. This is because it enables organizations to use a single model for application development, which results in fewer resources and less time spent on duplicating work. Model-driven DevOps also allows developers to create automated tests that are easier to maintain and run continuously. These automated tests can be used to determine whether the application is ready to be released and to identify defects in the application before it is delivered to customers.
• Another advantage of model-driven DevOps is that it may help to improve software quality. This is because it allows organizations to use automated testing and CI, which can catch defects and enforce code quality standards. Model-driven DevOps also enables organizations to use an iterative and agile approach to software development, which can help to reduce the risk of defects by allowing developers to make changes to the application as it is being developed instead of waiting until the end of a project when changes could be more costly and disruptive.
• A third advantage of model-driven DevOps is that it may lead to increased productivity. This is because it enables organizations to take advantage of automation, which can speed up activities such as code review and testing. Model-driven DevOps can also help to reduce bottlenecks by allowing teams to work together more efficiently.

The authors very clearly demonstrate how model-driven DevOps can help to improve software quality. However, it is also important to consider the following:

• If there are deficiencies in the current software development life cycle, model-driven DevOps may not be able to fix them. If a team is struggling to meet deadlines, has too much work in progress, or is lacking the necessary skills and tools, the introduction of model-driven DevOps may not be able to address these issues.
• If a team is using poor coding practices, automation tools will not be able to fix the issues with their application. Model-driven DevOps can only correct problems in the code that it can recognize.

When implementing model-driven DevOps, it is important to consider how it can be used to improve software quality and how it will impact the rest of the business. It is also important to communicate the benefits of model-driven DevOps to stakeholders and encourage them to embrace the approach. These are some key strategies for implementing model-driven DevOps:

• Establish a clear business case for model-driven DevOps.
• Before implementing model-driven DevOps, it is important to understand the possible benefits and how it can help improve software quality. Once the team has clarified their expectations, they should create a business case for model-driven DevOps and present it to other stakeholders. This will help to ensure that the approach is implemented successfully and meets the needs of the organization.
• Define clear governance and quality standards. As with any approach to software development, model-driven DevOps should be governed.

By understanding the principles of model-driven DevOps presented here, readers can take advantage of automation to shorten development cycles, increase productivity, and reduce costs. With a model-driven DevOps approach, organizations can develop applications faster and with greater agility and reliability.

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