About the Book
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
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.
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.