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Chapter 12
Microsoft Azure Notebooks
Microsoft offers Azure Notebooks, a free service for anyone to develop and run
code in their web-browser using Jupyter. It supports Python 2, Python 3, R and F#
and their popular packages. It is a general code authoring, executing, and sharing
platform. According to Microsoft documentation, one can use Notebooks in diverse
scenarios: like giving an online webinar, giving a PowerPoint-like presentation with
executable codes in slides, or learning a new model. The service is free. However,
to stop abuse, they have put network limitations; at present there is a 4 GB memory
limit per user, and a 1 GB data limit.
Azure Notebooks is a thriving place, with many existing and exciting notebooks
shared by the DL community. You can access them here: https://github.com/
jupyter/jupyter/wiki/A-gallery-of-interesting-Jupyter-Notebooks.
Like Colaboratory, Azure Notebooks have most packages preinstalled, and if you
require, you can install new packages via !pip install. You can run any Unix
command line command with a prefixing exclamation mark. To be able to use
Azure Notebooks, you will need to open an account. You can use your existing
Microsoft account, or create a new one. It even offers an option to create a Child
Account to encourage young people to learn programming, and these accounts
have parental control.
Some packages may not yet be available in Azure Notebooks.
You can access data directly through the notebook interface using upload/download
commands. You can even download data from a URL using !wget url_address.
Now that we've looked at the cloud services and the VMs that can help us to
perform our training, let's look at how we can move into the production stage,
using TensorFlow Extended.
TensorFlow Extended for production
TFX is an end-to-end platform for deploying machine learning pipelines. A part
of the TensorFlow ecosystem, it provides a configuration framework and shared
libraries so as to integrate the common components needed to define, launch, and
monitor software based on ML models. TFX includes many of the requirements for
production software deployments and best practices, viz: scalability, consistency,
testability, safety and security, and so on.
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