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 SageMaker

Amazon SageMaker provides a fully managed cloud platform for users to develop ML models from end to end. Some of the key features of Amazon SageMaker are as follows:

  • Data preparation: Amazon SageMaker provides various tools to preprocess and prepare data
  • Model training algorithms: SageMaker provides built-in algorithms for supervised learning, unsupervised learning, and reinforcement learning
  • Model deployment: After the ML model is trained and validated, SageMaker provides tools for model deployment, either as a batch transform job or a real-time endpoint
  • Scalability: SageMaker is a fully managed service, which means that AWS takes care of all the infrastructure and scaling, so the data scientists can focus on building better models rather than worrying about infrastructure
  • Integration: SageMaker integrates with other AWS services, such as S3, AWS Glue, and AWS Lambda, so data scientists can easily access and use datasets stored in AWS
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