Deep Learning in Production

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.