Journey to Become a Google Cloud Machine Learning Engineer: Build the mind and hand of a Google Certified ML professional

This is a fantastic book that combines the Google Cloud Platform and Machine Learning. The theme of the book is very clear, and the content ranges from basic to in-depth. It is perfect for anyone who wants to not only better understand the concept of Machine Learning in the cloud, but also for those who already have a good understanding and want to dive deep to become a professional Google-certified cloud Machine Learning engineer. The second section of the book is my favorite because it helps me understand the overall process of machine learning and the use of common models, which includes Chapter 3, Chapter 4, and Chapter 5.

This book is a great introduction to Google Cloud Platform and the basics of Machine Learning. It goes into details of how the Google Cloud platform works and the different functions of it. While also going into the basics of machine learning and its connectivity to the cloud platform. This book also has example of real-world use cases while providing a step-by-step guide of examples that can be created for practice.This would be a great asset for someone with an interest in cloud platforms or someone looking to obtain a certification in the Google Cloud Platform.

This book is a must-have guide for anyone serious about breaking into the world of machine learning and Google Cloud. It equips you with the right knowledge and tools to excel in your career. Whether you're a student or an industry professional, this book will undoubtedly take you to the next level.

“Journey to Become a Google Cloud Machine Learning Engineer” by Dr. Logan Song is a book highly recommended for anyone who would want to not just learn but truly understand the concept of Machine Language in cloud while preparing themselves to become a Google certified cloud ML engineer. I started reading the book with basic knowledge of Google Cloud which I thought would not be adequate, but the book helps you ease into the concepts and topics it provides which helped me get through the book instead of backing away. The flow of concepts in the book helps you understand the information while building a base for the succeeding concept. The author emphasizes that the best practice to learn ML is to study while simultaneously learning by performing the labs which helps understand how it works and how it reacts to the inputs submitted.The book starts off with an overview of the major sections of the GCP infrastructure which includes some topics like GCP structure, IAM, Storage, AI and many more which helps strengthen your base knowledge of GCP. The author includes some basics of Python which I felt was the best approach for a new reader like me to polish my python skills to work on GCP. Next was a step-by-step introduction to Machine Learning where it started off with defining ML problems while collecting data, testing, and deploying the model and the different types of Neural networks. The book introduced me to tools like Google Cloud BigQuery, BQML, TensorFlow, Keras and many more. I felt that the addition of the “Best Practices” information really helped me implement ML in GCP the right way. The detailed information provided in Part 5: Appendices helps ease through each practice session provided. This helped me not only run through the practice instructions but truly understand each command while also understanding how the tool reacts to the instruction. The few drawbacks I felt were present were that it is essential to have a good basic knowledge of GCP, it would take a while to ease into the lab practices and maybe more exam practice questions for more topics while also more questions for lab sessions where you can try different instructions to see how the system would react or what output would be provided.I would highly recommend this book for readers who plan to excel in Google Cloud Machine Learning while preparing themselves for the Google certification exam to become a Google certified Cloud Machine Language Engineer.

“Journey to become a Google Cloud Machine Learning Engineer” is a wonderful attempt by Dr. Logan Song. This book not only provides the necessary framework for anyone interested in pursuing Machine Learning in the cloud, but also acts as a guide for professionals trying to become Google certified ML Cloud Engineers.The book is divided into five parts with each part introducing the reader to the subjects in an eloquent manner. The biggest highlight of the book is the appendices in part five which are immensely helpful in getting a thorough practical experience on the topics discussed. The appendices 4 and 5 which provide labs for Google Vertex AI and Google ML API discussed in part three of the book are especially helpful in gaining valuable insights into how ML works in the cloud. The first part introduces the GCP infrastructure and services as well as the basics of the Python programming language whereas the second part takes us into the world of machine learning. The third part, which is the heart of the book, talks about the ML implementation in GCP. The fourth section acts as an able guide for anyone interested in pursuing GCP ML certification providing detailed analyses for the practice problems.One possible drawback of the book is that it introduces several topics briefly without going into depth. GCP and it’s resources should have been discussed a lot more in the book. Also, introducing the theoretical part along with some practical examples earlier in the sections would be helpful.Overall, the book is professionally written, and the ideas are wonderfully articulated by Dr. Song. I approached the book with only a basic knowledge of cloud computing and ML modeling but walked away with deeper insights into the subject. I highly recommend this book to anyone interested in learning about ML implementation in GCP.