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

An introduction to AI and ML

Artificial Intelligence (AI) is described as the ability of a digital computer to perform tasks that intelligent human beings can perform. ML is a subset of AI. It is used by machines to make decisions based on data without getting specific programmed instructions. It can be used, for example, to indicate whether an email that's been received is spam, to recognize objects, or to make smart predictions. The ML concept is illustrated in the following diagram:

Figure 14.1 – ML flow

As we can see, ML models rely on mathematical models that have been created from an analysis of samples called training data. The process of developing the model is called model training. The purpose of the model is to answer our question with the highest possible degree of accuracy. The better the accuracy, the better the model. You may be confused as to how this model is created. We will look at this in the following section.