The book "MLOps for Everyone" provides a detailed guide on how to build an ML enabled system using MLOps, its challenges, and tools to mitigate risks. The book provides an in-depth analysis of the people involved in MLOps, such as subject matter experts, data scientists, data engineers, software engineers, DevOps engineers, model risk managers, and machine learning architects.

The book also emphasizes why MLOps is necessary by providing a detailed overview of the basics of machine learning, including model development, production, and deployment, monitoring, and iteration, and life cycle. The book covers topics such as establishing business objectives, data sources, exploratory data analysis, feature engineering, and selection.

Furthermore, the book highlights the importance of reproducibility and responsible AI in model development and deployment. The book also covers the different model deployment types and requirements, including monitoring and concerns such as DevOps, data scientists, ground truth, input drift, and business concerns.

Overall, the book is a comprehensive guide that provides insights into the different aspects of MLOps and how to build an ML-enabled system using MLOps effectively. It is suitable for anyone interested in learning how to build and deploy machine learning models using MLOps, including subject matter experts, data scientists, data engineers, software engineers, DevOps engineers, model risk managers, and machine learning architects.