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TensorFlow and Cloud
• Scalability: As your need grows, you can scale your cloud resources as
simply as requesting a quota increase or a change in subscription model.
• Hassle-free: Unlike your personal system, where everything from choice of
hardware to the installation of software dependencies is your responsibility,
the cloud services offer ready-made solutions in the form of premade system
images. The images come installed with all the packages you might need for
training your deep learning model.
• Language support: All services support a variety of computer languages.
You can write your code in your favorite language.
• API for deployment: Most cloud services also allow you to embed your
deep learning model directly into the applications and on the web.
Depending on the services offered, the cloud platform can be classified as:
• Infrastructure as a Service (IaaS): In this case, the service provider only
provides the physical infrastructure; things like virtual machines, data
storage centers come in this.
• Platform as a Service (PaaS): Here, the service provider provides a runtime
environment, both hardware and software, for the development and
deployment of applications. For example, web servers and data centers.
• Software as a Service (SaaS): Here, the service provider provides a software
application as a service, for example Microsoft Office 365 or the interactive
Jupyter notebooks available on cloud.
Before delving into details about how to use different cloud services, let us go
through some popular cloud service providers and their offerings. We will be
considering Microsoft Azure, AWS, and Google Cloud platforms. All three of them
provide facilities to build, deploy, and manage applications, additionally they can
also provide services through the worldwide web.
Microsoft Azure
Microsoft Azure provides both PaaS and SaaS services. The Azure platform provides
a myriad of services: virtual machines, networking, storage, and even IoT solutions.
To access these services, you need to open an account with Azure. You will require
an email address to do this. Go to the site: https://azure.microsoft.com/en-in/
to open your account. The Azure platform also provides integration with GitHub,
so if you already have a GitHub account you can use it to log in. Once you have
successfully created the account and have logged in, you will see the following
dashboard:
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