Hands-On Serverless Deep Learning with TensorFlow and AWS Lambda

Use the serverless computing approach to save time and money

Key Features

• Save your time by deploying deep learning models with ease using the AWS serverless infrastructure
• Get a solid grip on AWS services and use them with TensorFlow for efficient deep learning
• Includes tips, tricks and best practices on serverless deep learning that you can use in a production environment

Book Description

One of the main problems with deep learning models is finding the right way to deploy them within the company's IT infrastructure. Serverless architecture changes the rules of the game—instead of thinking about cluster management, scalability, and query processing, it allows us to focus specifically on training the model. This book prepares you to use your own custom-trained models with AWS Lambda to achieve a simplified serverless computing approach without spending much time and money. You will use AWS Services to deploy TensorFlow models without spending hours training and deploying them. You'll learn to deploy with serverless infrastructures, create APIs, process pipelines, and more with the tips included in this book.

By the end of the book, you will have implemented your own project that demonstrates how to use AWS Lambda effectively so as to serve your TensorFlow models in the best possible way.

What you will learn

• Gain practical experience by working hands-on with serverless infrastructures (AWS Lambda)
• Export and deploy deep learning models using Tensorflow
• Build a solid base in AWS and its various functions
• Create a deep learning API using AWS Lambda
• Look at the AWS API gateway
• Create deep learning processing pipelines using AWS functions
• Create deep learning production pipelines using AWS Lambda and AWS Step Function

Who this book is for

This book will benefit data scientists who want to learn how to deploy models easily and beginners who want to learn about deploying into the cloud. No prior knowledge of TensorFlow or AWS is required.