Book Image

The Self-Taught Cloud Computing Engineer

By : Dr. Logan Song
Book Image

The Self-Taught Cloud Computing Engineer

By: Dr. Logan Song

Overview of this book

The Self-Taught Cloud Computing Engineer is a comprehensive guide to mastering cloud computing concepts by building a broad and deep cloud knowledge base, developing hands-on cloud skills, and achieving professional cloud certifications. Even if you’re a beginner with a basic understanding of computer hardware and software, this book serves as the means to transition into a cloud computing career. Starting with the Amazon cloud, you’ll explore the fundamental AWS cloud services, then progress to advanced AWS cloud services in the domains of data, machine learning, and security. Next, you’ll build proficiency in Microsoft Azure Cloud and Google Cloud Platform (GCP) by examining the common attributes of the three clouds while distinguishing their unique features. You’ll further enhance your skills through practical experience on these platforms with real-life cloud project implementations. Finally, you’ll find expert guidance on cloud certifications and career development. By the end of this cloud computing book, you’ll have become a cloud-savvy professional well-versed in AWS, Azure, and GCP, ready to pursue cloud certifications to validate your skills.
Table of Contents (24 chapters)
1
Part 1: Learning about the Amazon Cloud
9
Part 2:Comprehending GCP Cloud Services
14
Part 3:Mastering Azure Cloud Services
19
Part 4:Developing a Successful Cloud Career

ML basics and ML pipelines

What is ML? ML is a subfield of artificial intelligence (AI) that focuses on building models and algorithms to learn patterns and relationships from data and make predictions or decisions. A typical ML project involves the following process – the so-called ML pipeline:

  • Problem framing: Define ML problems from business projects
  • Data collection: Collect data from various sources, which may involve data labeling
  • Data evaluation: Examine the data using statistical tools
  • Feature engineering: Select and extract model features and targets
  • Model training: Train the model with the training dataset
  • Model verification: Verify the model with the verification dataset
  • Model testing: Test the model with the testing dataset
  • Model deployment: Deploy the ML model to production

Figure 6.1 shows the ML pipeline, which is an iterative process to collect data and develop ML models for deployment:

Figure 6.1 – ML pipeline

Figure...