Production-Ready Applied Deep Learning

Supercharge your skills for developing powerful deep learning models and distributing them at scale efficiently using cloud services

Key Features
Understand how to execute a deep learning project effectively using various tools available
Learn how to develop PyTorch and TensorFlow models at scale using Amazon Web Services
Explore effective solutions to various difficulties that arise from model deployment
Book Description
Machine learning engineers, deep learning specialists, and data engineers encounter various problems when moving deep learning models to a production environment. The main objective of this book is to close the gap between theory and applications by providing a thorough explanation of how to transform various models for deployment and efficiently distribute them with a full understanding of the alternatives.

First, you will learn how to construct complex deep learning models in PyTorch and TensorFlow. Next, you will acquire the knowledge you need to transform your models from one framework to the other and learn how to tailor them for specific requirements that deployment environments introduce. The book also provides concrete implementations and associated methodologies that will help you apply the knowledge you gain right away. You will get hands-on experience with commonly used deep learning frameworks and popular cloud services designed for data analytics at scale. Additionally, you will get to grips with the authors' collective knowledge of deploying hundreds of AI-based services at a large scale.

By the end of this book, you will have understood how to convert a model developed for proof of concept into a production-ready application optimized for a particular production setting.

What you will learn
Understand how to develop a deep learning model using PyTorch and TensorFlow
Convert a proof-of-concept model into a production-ready application
Discover how to set up a deep learning pipeline in an efficient way using AWS
Explore different ways to compress a model for various deployment requirements
Develop Android and iOS applications that run deep learning on mobile devices
Monitor a system with a deep learning model in production
Choose the right system architecture for developing and deploying a model
Who this book is for
Machine learning engineers, deep learning specialists, and data scientists will find this book helpful in closing the gap between the theory and application with detailed examples. Beginner-level knowledge in machine learning or software engineering will help you grasp the concepts covered in this book easily.

Table of Contents
Effective Planning of Deep Learning-Driven Projects
Data Preparation for Deep Learning Projects
Developing a Powerful Deep Learning Model
Experiment Tracking, Model Management, and Dataset Versioning
Data Preparation in the Cloud
Efficient Model Training
Revealing the Secret of Deep Learning Models
Simplifying Deep Learning Model Deployment
Scaling a Deep Learning Pipeline
Improving Inference Efficiency
Deep Learning on Mobile Devices
Monitoring Deep Learning Endpoints in Production
Reviewing the Completed Deep Learning Project