Deep Learning in Production

Deep Learning in Production
by Sergios Karagiannakos
Publisher Finelybook 出版社: Sergios Karagiannakos (November 24, 2021)
Language 语言: English
pages 页数: 223 pages
ISBN-10 书号: 6180033773
ISBN-13 书号: 9786180033779

Book Description
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
Building an End-to-End Pipeline

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