The application of agile principles to applied machine learning teams is the topic of this book. Machine learning relies heavily on data. This poses new types of challenges, that is, data is generally noisy, follows unknown patterns, and can be biased. The authors tackle these challenges by customizing the agile development principles in order to deliver a high-quality, data-oriented product.

The book is comprised of 13 chapters. Each chapter aligns an agile principle with a set of machine learning project requirements. Chapter 1 discusses the need for more data attributes and quality to be ready for early delivery. Chapter 2 proposes new pipelines and models to support the ever-changing requirements in software development. Chapter 3 emphasizes the importance of data verification and continuous deployment to satisfy the continuous delivery requirement.

Chapter 4 discusses the importance of clear communication with stakeholders to understand data, for the sake of aligning with business. Chapter 5 discusses the importance of recruiting motivated individuals and providing the right working environment for them. Chapter 6 covers different data tools for effective communication between stakeholders.

Monitoring is a critical part in the software development process. Chapter 7 details its importance in the context of data-oriented products. Chapter 8 discusses the importance of sustainable awareness and how to adjust the development pace. Chapter 9 reiterates the importance of software engineering best practices in the context of data-oriented product development.

Chapter 10 emphasizes the importance of well-defined tasks. Chapter 11 discusses how teams are at the heart of the development process. This chapter lists the main strategies that help build motivated and self-organizing teams. Chapter 12 enumerates the main strategies used to tune and adjust the final product. Chapter 13 is a summary, providing a mapping of agile manifesto principles with suggested modifications for agile machine learning teams.

It is worth mentioning that the current publication is the result of the authors’ experience with machine learning projects. The book is a good attempt at tackling the challenges that come with such projects. It is concise and informative, but definitely not intended for readers with little knowledge of the general software engineering process. I would recommend it to experienced managers and software engineers who are planning to work on machine learning projects. The provided guidelines will help readers avoid common pitfalls and hand over high-quality deliverables.

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