![]() ![]() Notice in Figure 1-1 that the Amazon S3 storage system can seamlessly handle the scaling of computing, storage, and disk I/O with any number of workers that access the resource. This new workflow allows users and systems to operate data where it resides versus moving it back and forth to workstations or specialized file servers. Through elastic compute and storage, data lake capability via Amazon S3 opens up workflows that didn’t exist previously. Deep learning requires access to large quantities of storage, disk I/O, and compute. Machine learning is an excellent example of an ideal use case for “near infinite” computing resources. The illusion of infinite computing resources This step accomplishes two things in learning cloud computing: teaching yourself better metacognition skills and building a portfolio of work that makes you more marketable. Similarly, in this book, I recommend building out a series of GitHub projects that catalog your work and then building out demo videos explaining how your project works. One of the ways I encourage this in a classroom is by facilitating students to demo progress weekly on what they are working on and build a portfolio of work. In practice, this means trying things out, getting frustrated, then working out the best way to solve what is frustrating you, and then doing that over and over again as quickly as possible. A key performance indicator, or KPI, is how many mistakes you can make per week. Having taught thousands of students and working professionals cloud computing, I (Noah) have strong opinions on learning as quickly as possible. Let’s get started with a brief dive into cloud computing. These exercises are an excellent tool for creating a comprehensive portfolio of work that gets you hired. NET developers.įor scripting fans, there are also examples of using the AWS Command Line Interface and PowerShell for the AWS SDK.Īt the end of the chapter, there are discussion questions and exercises that you can use to build on the lessons covered. ![]() Additionally, both traditional development methods like Visual Studio and new cloud native strategies like developing with AWS Cloud9 are covered. You can view those code examples in the source code repository for the book. The material in this book sets you up for success by adding many code examples of doing. These essentials include using cloud-based development environments such as AWS CloudShell and a traditional Visual Studio development environment that leverages the AWS SDK. This chapter covers the basic scaffolding of essentials on your first day working with AWS and C#. ![]()
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