Build, train, deploy, scale and maintain deep learning models. Understand ML infrastructure and MLOps using hands-on examples.
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
Book description
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.
Table of Contents
Designing a machine learning system
Setting up a Deep Learning Workstation
Writing and Structuring Deep Learning Code
Data Processing
Training
Serving
Deploying
Scaling
Building an End-to-End Pipeline
Editorial Reviews
Review
“Sergios made an exceptional job in covering a wide range of machine and deep learning topics. In parallel, the majority of the topics are explored at an adequate level for an entry-level engineer/researcher that wants more hands-on examples. Overall the book 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 was the case for myself. There were many crucial additions to bridge this particular gap. Finally, a great effort was made to focus on the principles behind the actual tools presented in this book. In this respect, I sincerely believe that the book combines the best of both worlds.” — Nikolas Adaloglou, PhD AI researcher
“Sergios book, Deep Learning in Production, distills the essence for the steps needed for deployment of deep learning models in a clear and concise way. The book provides a clear path from designing a system to production and monitoring while using best software development practices. I think it’s a great resource for anyone that wants to see how their models behave in the real world!” —
“One of the best choices out there for getting a complete overview of the tasks needed to build an End-to-End machine learning pipeline. “Deep Learning in Production” aims to guide ML/AI enthusiasts to design a complete and robust AI system by introducing them to fundamental development concepts such as deploying scalable containerized applications in the cloud while testing and monitoring them. Hands-on examples make this book ideal for entry-level developers and highlight the importance of each step in the process.” —
Mihalis Gongolidis, Data Engineer
From 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.
From the Back Cover
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.
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