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TensorFlow and Cloud
AI algorithms require extensive computing resources. With the availability of
a large number of cloud platforms offering their services at competitive prices,
cloud computing offers a cost-effective solution. In this chapter, we will talk about
three main cloud platform providers that occupy the majority of the market share:
Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform.
Moreover, once you have trained your model on cloud, you can use TensorFlow
Extended (TFX) to move your model to production. The chapter will cover:
• Creating and using virtual machines on cloud
• Creating and training directly on Jupyter Notebook on cloud
• Deploying the model on cloud
• Using TFX for production
• TensorFlow Enterprise
Deep learning on cloud
There was a time when, if you wanted to work in the field of deep learning, then
you needed to shell out thousands of dollars to obtain the infrastructure required to
train your deep learning model. Not anymore! Today, a large number of public cloud
service providers offer affordable cloud computing services. Training your Deep
Learning (DL) model on cloud offers various advantages:
• Affordability: Most cloud service providers offer a range of subscription
options; you can choose from monthly subscriptions to pay-as-you-use
options. Most also offer free credit for new users.
• Flexibility: You are no longer bound to a physical location; you can log in
to the cloud from any physical location and continue your work.
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