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TensorFlow and Cloud
You just share the link and the other person can view it and run it, without any of the
hassle of OS environment and software dependencies. In this section we will cover
the Jupyter Notebook environments made available by three of the technological
giants: Google, Microsoft, and Amazon.
SageMaker
Amazon SageMaker is a fully managed machine learning service. You can use it
easily and quickly build and train machine learning models. The trained models can
then be directly deployed into a production-ready hosted environment. SageMaker
provides an integrated Jupyter notebook instance; this allows for easy access to data
sources and provides a convenient coding platform for exploration and analysis,
thus removing any need to manage servers.
An additional feature provided by SageMaker is the availability of optimized
common machine learning algorithms. This allows users to run code efficiently,
even when the dataset being used is extremely large. It offers flexible distributed
training options that you can tailor according to your specific workflow. The trained
model can later be deployed into a scalable and secure environment, with only
a single click from the Amazon SageMaker console. Both training and hosting are
billed according to the number of minutes used. There are no minimum fees and no
upfront commitments. You can follow the Amazon documentation on how to setup
SageMaker using this link: https://docs.aws.amazon.com/sagemaker/latest/
dg/gs.html.
In order to load data and deploy your model, you will need to use SageMaker
modules and functions. A good place to start will be this tutorial: https://www.
bmc.com/blogs/amazon-sagemaker/. As you may gather from the tutorial, Amazon
SageMaker is not free. Even experimenting on it to write code for this book required
us to spend precious dollars. However, it offers ease of deployment.
Google Colaboratory
Google, along with the Jupyter development team, launched Google Colaboratory
in 2014. Since then, the Colaboratory has grown in function and utility. Today, it
supports GPU and TPU hardware acceleration. It supports Python (2.7 and 3.6
version). The Colab is integrated with Google Drive, so your notebooks are saved
on your drive and you can also read data from your own drive (you will need to
authorize the notebook first).
The best part of Google Colaboratory is that it is completely free. You can run your
code continuously for 12 hours on it. To be able to work with Colaboratory, you
need an account with Google. Your normal Gmail account will also work.
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