Building Machine Learning Systems with Python - Richert, Coelho

Chapter 12
Summary
We saw how to use jug, a little Python framework, to manage computations in a
way that takes advantage of multiple cores or multiple machines. Although this
framework is generic, it was built specifically to address the data analysis needs of
its author (who is also an author of this book). Therefore, it has several aspects that
make it fit in with the rest of the Python machine learning environment.
We also learned about AWS, the Amazon cloud. Using cloud computing is often
a more effective use of resources than building an in-house computing capacity.
This is particularly true if your needs are not constant, but changing. Starcluster
even allows for clusters that automatically grow as you launch more jobs and shrink
as they terminate.
This is the end of the book. We have come a long way. We learned how to perform
classification when we have labeled data and clustering when we do not. We learned
about dimensionality reduction and topic modeling to make sense of large datasets.
Towards the end, we looked at some specific applications, such as music genre
classification and computer vision. For implementations, we relied on Python. This
language has an increasingly expanding ecosystem of numeric computing packages
built on top of NumPy. Whenever possible, we relied on scikit-learn but also used
other packages when necessary. Due to the fact that they all use the same basic data
structure (the NumPy multidimensional array), it is possible to mix functionality
from different packages seamlessly. All of the packages used in this book are open
source and available for use in any project.
Naturally, we did not cover every machine learning topic. In the Appendix, Where
to Learn More About Machine Learning, we provide pointers to a selection of other
resources that will help interested readers learn more about machine learning.
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