Deep Learning in Production

by Sergios Karagiannakos

Build, train, deploy, scale and maintain deep learning models. Understand ML infrastructure and MLOps using hands-on examples.

Deep Learning research is advancing rapidly over the past years. Frameworks and libraries are constantly been developed and updated. However, we still lack standardized solutions on how to serve, deploy and scale Deep Learning models. Deep Learning infrastructure is not very mature yet.

This book accumulates a set of best practices and approaches on how to build robust and scalable machine learning applications. It covers the entire lifecycle from data processing and training to deployment and maintenance. It will help you understand how to transfer methodologies that are generally accepted and applied in the software community, into Deep Learning projects.

It's an excellent choice for researchers with a minimal software background, software engineers with little experience in machine learning, or aspiring machine learning engineers.

What you will learn?

- Best practices to write Deep Learning code
- How to unit test and debug Machine Learning code
- How to build and deploy efficient data pipelines
- How to serve Deep Learning models
- How to deploy and scale your application
- What is MLOps and how to build end-to-end pipelines

Who is this book for?

- Software engineers who are starting out with deep learning
- Machine learning researchers with limited software engineering background
- Machine learning engineers who seek to strengthen their knowledge
- Data scientists who want to productionize their models and build customer-facing applications

What tools you will use?

Tensorflow, Flask, uWSGI, Nginx, Docker, Kubernetes, Tensorflow Extended, Google Cloud, Vertex AI

223 pages, Paperback

Published November 24, 2021

About the author

Sergios Karagiannakos is a Machine Learning Engineer with a focus on ML infrastructure and MLOps.

He has worked with several companies towards building and deploying Artificial Intelligence applications. During his last position in the ML infrastructure team at Hubspot, he helped build and maintain all Machine Learning services and pipelines inside the organization, serving more than 1 billion requests per day.

He graduated with a Master’s in Electrical and Computer Engineering from the University of Patras, and he then joined Eworx SA as a Data Scientist. Afterward, he worked as an independent ML engineer with small startups, and in 2019, he founded AI Summer, an educational platform around Deep Learning. During this time, he has authored more than 50 articles and published the Introduction to Deep Learning & Neural Networks course.

Interesting facts: He was included in the Top 100 influential voices and brands in Data Science and Deep Learning, he strives to bring the entire Greek tech community together, and he really wishes that Artificial General Intelligence will be solved in our lifetime.

Reviews

[Nikolas · Rating 5 out of 5]

I was an editor of the book. It was written carefully to be as self-complete as possible. I find it a great resource for people from academia and research who want to move into the ML business world, as it was the case for myself. There were many additions to bridge this particular gap.

[Sergios Karagiannakos · Rating 5 out of 5]

Full disclaimer: I'm the author. Deep Learning in Production is a product of one year of effort. The pages and the code you will read began as articles on our blog "AI Summer" and they were later combined and organized into a single resource. Some were rewritten from scratch; some were modified to fit the book's structure. Plus, we added completely new material!

The reason I decided to invest the time in writing this book is simple. The practices and principles inside are what I wish I knew when I started my journey on machine learning. This complete, standalone guide -- outlining every aspect of the deep learning pipeline -- would've accelerated x10 my learning curve. I do hope that it will do the same for you.

At the very least, it'll demystify the industry and provide a holistic overview of all of its different branches.

You'll hold the knowledge and first-hand experience I've accumulated over the past years working as part of the machine learning infrastructure team at HubSpot, a Data Scientist in a web agency, and an independent contractor for various start-ups. Each project helped me learn something new, and each team gave me a fresh and unique perspective on the field.

The insider info and skills you'll acquire from this book will provide you with better job opportunities, will differentiate you from other data scientists and machine learning researchers.

But the most important thing: they will make you a better and more well-rounded engineer.

[Ilias Iliopoulos · Rating 5 out of 5]

I am big fan of Sergios' and Nick's work in AiSummer and I was very excited to read the book once it came out. Great material with solid and thorough explanations on topics we all deal with daily in Deep Learning. If you liked the AiSummer articles you are going to LOVE this book! Absolutely recommmend it!!!

[Mihalis Zournatzis · Rating 5 out of 5]

I highly recommend this book. It is really impressive how well written this book is, as it makes it really easy for the reader to understand difficult concepts.

[Andreas Stergioulas · Rating 5 out of 5]

The book is very easy to follow and you can get from zero to hero just by reading it!

[Sofia Adaloglou · Rating 5 out of 5]

Challenge to read !!!! Thoroughly worked and clearly written so as to provide a deep insight into infrastructure and MLOps . Value for time and money !

[Greg · Rating 5 out of 5]

I really enjoyed this book. Interesting content and and so easy to follow. Congratulations to the authors

[Vincent Wang · Rating 5 out of 5]

Very practical book. Will need to reread carefully again in the future once needed.