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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

Azure ML workspaces

An Azure ML workspace allows you to build, deploy, and manage ML models at scale. It provides a centralized workspace for data scientists, machine learning engineers, and developers to collaborate on machine learning projects, with the following features:

  • An Azure ML workspace is an end-to-end suite for organizing and managing ML assets such as datasets, models, notebooks, experiments, and pipelines/resources. It provides a centralized location for team collaboration, version control, and resource management.
  • It integrates with Jupyter notebooks and provides an interactive environment for developing and running code, visualizing data, and documenting the ML process.
  • It supports dataset versioning and management so you can register and track different versions of datasets for ML model training and evaluation. Datasets can be stored within the workspace or referenced from external data sources.
  • It allows you to organize and track different iterations...
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The Self-Taught Cloud Computing Engineer
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