Deep Learning in Production
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Deep Learning in Production

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

 

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.

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  • Categories

    • Machine Learning
    • Google Cloud Platform
    • Artificial Intelligence
    • Software Architecture
    • Python
    • Docker
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About the Author

Sergios Karagiannakos
Sergios Karagiannakos

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.

Table of Contents

Preface

Acknowledgements

1. About this Book

1.1 Welcome to Deep Learning in Production

1.2 Is this book for me?

1.3 What is the book’s goal?

1.4 Will this be difficult to learn?

1.5 Why should you read this book?

1.6 How to use this book?

1.7 How is the book structured?

1.8 Do I need to know anything else before I get started?

2. Designing a Machine Learning System

2.1 Machine learning: phase zero

2.2 Data engineering

2.3 Model engineering

2.4 DevOps engineering

2.5 Putting it all together

2.6 Tackling a real-life problem

3. Setting up a Deep Learning Workstation

3.1 Laptop setup

3.2 Frameworks and libraries

3.3 Development tools

3.4 Python package and environment management

4. Writing and Structuring Deep Learning Code

4.1 Best practices

4.2 Unit testing

4.3 Debugging

5. Data Processing

5.1 ETL: Extract, Transform, Load

5.2 Data reading

5.3 Processing

5.4 Loading

5.5 Optimizing a data pipeline

6. Training

6.1 Building a trainer

6.2 Training in the cloud

6.3 Distributed training

7. Serving

7.1 Preparing the model

7.2 Creating a web application using Flask

7.3 Serving with uWSGI and Nginx

7.4 Serving with model servers

8. Deploying

8.1 Containerizing using Docker and Docker Compose

8.2 Deploying in a production environment

8.3 Continuous Integration and Delivery (CI / CD)

9. Scaling

9.1 A journey from 1 to millions of users

9.2 Growing with Kubernetes

10. Building an End-to-End Pipeline

10.1 MLOps

10.2 Building a pipeline using TFX

10.3 MLOps with Vertex AI and Google Cloud

10.4 More end-to-end solutions

11. Where to Go from Here

Appendix

Table of Figures

About the Author

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