What you will learn?
Who is this book for?
What tools you will use?
Tensorflow, Flask, uWSGI, Nginx, Docker, Kubernetes, Tensorflow Extended, Google Cloud, Vertex AI
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.
The books is based on an article series published in 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!
If you like our effort, don't forget to star the project :) It matters!