Deep Learning in Production
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
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
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
5. Data Processing
5.1 ETL: Extract, Transform, Load
5.2 Data reading
5.5 Optimizing a data pipeline
6.1 Building a trainer
6.2 Training in the cloud
6.3 Distributed training
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
8.1 Containerizing using Docker and Docker Compose
8.2 Deploying in a production environment
8.3 Continuous Integration and Delivery (CI / CD)
9.1 A journey from 1 to millions of users
9.2 Growing with Kubernetes
10. Building an End-to-End Pipeline
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
Table of Figures
About the Author
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