Book Image

Professional Cloud Architect Google Cloud Certification Guide - Second Edition

By : Konrad Cłapa, Brian Gerrard
5 (1)
Book Image

Professional Cloud Architect Google Cloud Certification Guide - Second Edition

5 (1)
By: Konrad Cłapa, Brian Gerrard

Overview of this book

Google Cloud Platform (GCP) is one of the industry leaders thanks to its array of services that can be leveraged by organizations to bring the best out of their infrastructure. This book is a comprehensive guide for learning methods to effectively utilize GCP services and help you become acquainted with the topics required to pass Google's Professional Cloud Architect certification exam. Following the Professional Cloud Architect's official exam syllabus, you'll first be introduced to the GCP. The book then covers the core services that GCP offers, such as computing and storage, and takes you through effective methods of scaling and automating your cloud infrastructure. As you progress through the chapters, you'll get to grips with containers and services and discover best practices related to the design and process. This revised second edition features new topics such as Cloud Run, Anthos, Data Fusion, Composer, and Data Catalog. By the end of this book, you'll have gained the knowledge required to take and pass the Google Cloud Certification – Professional Cloud Architect exam and become an expert in GCP services.
Table of Contents (25 chapters)
1
Section 1: Introduction to GCP
5
Section 2: Manage, Design, and Plan a Cloud Solution Architecture
14
Chapter 12: Exploring Storage and Database Options in GCP – Part 2
17
Section 3: Secure, Manage and Monitor a Google Cloud Solution
21
Section 4: Exam Focus

Summary

In this chapter, we learned about the ML services offered by GCP. We started with the theory of ML to introduce basic concepts and nomenclature to better understand the actual services. We learned that, depending on your role and use case, you need to make the correct choice as to which service will be the most effective for you to use. One goal can sometimes be achieved using two or more different services. We also learned that you don't need to be a data scientist to leverage ML. Those of you who have very limited knowledge can use pretrained models. If those models are not good enough for your use case, you can try AutoML, which allows new models to be created without us having to develop the model ourselves. We just need to deliver proper datasets to GCP.

Finally, for those of you who have the knowledge and are capable of developing your own models, Vertex AI is the service you can use to develop and host your models.

In the next chapter, we will have a closer...