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

Amazon Machine Learning Services

We discussed cloud databases and big data analytics in previous chapters. Part of the data analytics spectrum, machine learning (ML) involves building models or algorithms that enable computers to analyze and learn from data, identify patterns, relationships, and trends that can be used to make predictions or decisions.

Cloud-based ML platforms provide a range of tools and services to support ML workflows of data preparation, feature engineering, model training, tuning, and deployment. Cloud ML can be used for computer vision, natural language processing (NLP), and many other predictive analytics tasks. The Amazon cloud provides platforms for engineers and data scientists to develop ML models from end to end. In this chapter, we will discuss the following topics:

  • ML basics: What is ML? What are the objectives of ML? What problems can be solved using ML? What are some basic ML problems?
  • Amazon SageMaker: A fully managed AWS ML service...