welcome
Dear Reader,
Thanks for purchasing the MEAP of Graph-Powered Machine Learning. Graph-based machine
learning is becoming a very important trend in Artificial Intelligence, transcending a lot of
other techniques. Google, Facebook, and E-bay – to cite some of them - have multiple
projects involving graphs, and more specifically graph models and graph algorithms, as
empowering mechanism behind the most advanced services they are providing to their end
users.
Graph-Powered Machine Learning is a practical guide to effectively using graphs in machine
learning applications, driving you in all the stages necessary for building complete solutions
where graphs play a key role. It focuses on methods, algorithms, and design patterns related
to graphs. Based on my personal experience on building complex machine learning
applications, this book suggests many recipes in which graphs are the main ingredient to
prepare a tasty product to your customers. Across the lifecycle of a machine learning project
such approaches can be useful under several aspects: managing data sources more efficiently,
implement better algorithms, storing the prediction model so that they can be accessed faster,
and visualizing the results in a more effective way for further analysis.
The book is divided into three parts. The first part is introductory to the topic. The three
chapters introduce main graph and machine learning concepts from the basics. Furthermore,
the role of graphs in Big Data Platforms and Machine learning is highlighted and presented
using multiple scenarios.
The second part is the core of the book. Several techniques are described, from data
source modeling, to algorithm design both leveraging graphs as underlying technology. A lot
of optimization approaches, best practices, and common pitfalls are detailed to help data
scientists or data engineers define the infrastructure and choose the right approaches since
the beginning of their projects.
The last part presents three different applications. Here concrete end-to-end projects are
discussed and for each of them the architecture, design best practice, and common pitfalls will
be illustrated.
I hope that what you'll get access to will be of interest for your current machine learning
project and for your learning path as a data scientist, a data engineer or a data architect, and
that it will occupy an important place on your digital or, even better, physical bookshelf.
Please be sure to stop by the Author’s forum with any feedback you have. With your help,
I’m sure the final book will be great!
Yours,
—Alessandro Negro
©Manning Publications Co. We welcome reader comments about anything in the manuscript - other than typos and
other simple mistakes. These will be cleaned up during production of the book by copyeditors and proofreaders.
https://forums.manning.com/forums/graph-powered-machine-learning
