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

Learning models

Now that we have a basic understanding of ML and how to train a model, let's have a look at three types of ML learning. These are as follows:

  • Supervised learning
  • Unsupervised learning
  • Semi-supervised ML

The preceding three types of ML are defined as follows:

  • Supervised learning: Supervised learning is the most common model. It is used when the training data and validation data are labeled. What the model does is learn how to set a label for input data. It does this based on what it has learned from some labeled training data. We can further classify supervised learning into the following categories:
    • Classification: This occurs when the output data is a category, such as apple, pear, or orange.
    • Regression: This occurs when the output data is a value, such as cost or temperature.
  • Unsupervised learning: Unsupervised learning is used when the training data is not labeled. The model attempts to learn the structure of the data and export...