Hands-On Serverless Deep Learning with TensorFlow and AWS Lambda: Training serverless deep learning models using the AWS infrastructure

Rustem Feyzkhanov

相關主題

商品描述

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.

Table of Contents

  1. Beginning with Serverless Computing and AWS Lambda
  2. Start deploying with AWS Lambda functions
  3. Start deploying TensorFlow models
  4. Working with Tensorflow on AWS Lambda
  5. Creating deep learning API
  6. Creating deep learning pipeline
  7. Creating deep learning workflow

商品描述(中文翻譯)

**使用無伺服器計算方法來節省時間和金錢**

主要特點
- 藉由使用 AWS 無伺服器基礎設施輕鬆部署深度學習模型,節省您的時間
- 深入了解 AWS 服務,並與 TensorFlow 一起使用,以提高深度學習的效率
- 包含在生產環境中可使用的無伺服器深度學習技巧、竅門和最佳實踐

書籍描述
深度學習模型的一個主要問題是如何在公司的 IT 基礎設施中找到合適的部署方式。無伺服器架構改變了遊戲規則——不再需要考慮叢集管理、可擴展性和查詢處理,而是讓我們專注於模型的訓練。本書將幫助您使用自定義訓練的模型與 AWS Lambda,實現簡化的無伺服器計算方法,而不需花費太多時間和金錢。您將使用 AWS 服務來部署 TensorFlow 模型,而無需花費數小時進行訓練和部署。您將學會如何使用無伺服器基礎設施進行部署,創建 API、處理管道等,並獲得本書中包含的技巧。

在本書結束時,您將實現自己的專案,展示如何有效使用 AWS Lambda,以最佳方式服務您的 TensorFlow 模型。

您將學到的內容
- 通過實際操作無伺服器基礎設施(AWS Lambda)獲得實踐經驗
- 使用 TensorFlow 匯出和部署深度學習模型
- 在 AWS 及其各種功能上建立堅實的基礎
- 使用 AWS Lambda 創建深度學習 API
- 了解 AWS API Gateway
- 使用 AWS 函數創建深度學習處理管道
- 使用 AWS Lambda 和 AWS Step Function 創建深度學習生產管道

本書適合誰
本書將惠及希望輕鬆學習如何部署模型的數據科學家,以及希望了解如何在雲端進行部署的初學者。無需具備 TensorFlow 或 AWS 的先前知識。

目錄
1. 開始無伺服器計算和 AWS Lambda
2. 開始使用 AWS Lambda 函數進行部署
3. 開始部署 TensorFlow 模型
4. 在 AWS Lambda 上使用 TensorFlow
5. 創建深度學習 API
6. 創建深度學習管道
7. 創建深度學習工作流程