DL Book 9 - Deploying Deep Learning Models in Production for Rookies

Table of Contents

1. Introduction

1.1 What is Deep Learning?

1.2 Why Deploy Deep Learning Models in Production?

1.3 Challenges and Best Practices of Deploying Deep Learning Models 1.4 Overview of the Book

2. Preparing Deep Learning Models for Deployment

2.1 Choosing the Right Framework and Library

2.2 Training and Validating Deep Learning Models

2.3 Saving and Loading Deep Learning Models

2.4 Optimizing Deep Learning Models for Inference

3. Deploying Deep Learning Models on Cloud Platforms

3.1 Overview of Cloud Computing Services

3.2 Deploying Deep Learning Models on AWS

3.3 Deploying Deep Learning Models on Azure

3.4 Deploying Deep Learning Models on Google Cloud

4. Deploying Deep Learning Models on Edge Devices

4.1 Overview of Edge Computing and IoT

4.2 Deploying Deep Learning Models on Mobile Devices

4.3 Deploying Deep Learning Models on Raspberry Pi

4.4 Deploying Deep Learning Models on Jetson Nano

5. Monitoring and Updating Deep Learning Models in Production

5.1 Logging and Debugging Deep Learning Models

5.2 Evaluating and Improving Deep Learning Models 5.3 Scaling and Load Balancing Deep Learning Models 5.4 Securing and Maintaining Deep Learning Models