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Machine Learning Infrastructure and Best Practices for Software Engineers” by Miroslaw Staron is a book aimed at software engineers who want to take their machine learning (ML) projects from prototype to full-fledged software systems. Here’s a breakdown of what the book offers and some factors to consider before picking it up:

Potential Benefits:

Focus on Software Engineering Practices: The book bridges the gap between data science and software engineering, emphasizing the practical aspects of building and deploying ML models in production environments.
Scalability and Robustness: It delves into building scalable and robust ML pipelines that can handle real-world use cases.
Software Development Best Practices: You’ll learn about applying best practices from software engineering to ensure the quality and maintainability of your ML projects.
Data Considerations: The book explores essential topics like data quality, ethical considerations in ML, and choosing the right data sources.
Deployment Strategies: It covers different deployment strategies for ML models, including cloud platforms and on-premise solutions.
Points to Consider:

Target Audience: The book is geared towards software engineers with some background in machine learning concepts.
Depth of ML Knowledge: While it covers foundational ML concepts, it might not delve into the intricate details of specific ML algorithms.
Focus on Practices: The book emphasizes practical implementation over in-depth theoretical explanations of ML algorithms.
Who Can Benefit:

Software Engineers Transitioning to ML: This book is a great resource for software engineers with programming experience who want to build and deploy real-world ML applications.
Data Science Teams with Engineering Backgrounds: Data science teams with software engineering skills can benefit from the book’s focus on best practices and production-ready ML systems.

Machine Learning Crash Course by Google: (Free online course offering a good introduction to ML concepts)
Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow by Aurélien Géron: (A comprehensive book that dives deeper into building and evaluating ML models)