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  • Book Overview & Buying The Self-Taught Cloud Computing Engineer
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The Self-Taught Cloud Computing Engineer

The Self-Taught Cloud Computing Engineer

By : Dr. Logan Song
5 (180)
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The Self-Taught Cloud Computing Engineer

The Self-Taught Cloud Computing Engineer

5 (180)
By: Dr. Logan Song

Overview of this book

As cloud computing continues to revolutionize IT, professionals face the challenge of keeping up with rapidly evolving technologies. This book provides a clear roadmap for mastering cloud concepts, developing hands-on expertise, and obtaining professional certifications, making it an essential resource for those looking to advance their careers in cloud computing. Starting with a focus on the Amazon cloud, you’ll be introduced to fundamental AWS cloud services, followed by 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, differentiating their unique features, along with leveraging real-life cloud project implementations on these cloud platforms. Through hands-on projects and real-world applications, you’ll gain the skills needed to work confidently across different cloud platforms. The book concludes with career development guidance, including certification paths and industry insights to help you succeed in the cloud computing landscape. Walking through this cloud computing book, you’ll systematically establish a robust footing in AWS, Azure, and GCP, and emerge as a cloud-savvy professional, equipped with cloud certificates to validate your skills.
Table of Contents (24 chapters)
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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...

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The Self-Taught Cloud Computing Engineer
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