Designing Deep Learning Systems: A Software Engineer's Guide

A vital guide to building the platforms and systems that bring deep learning models to production.

Summary

In Designing Deep Learning Systems you will learn how to:

• Transfer your software development skills to deep learning systems
• Recognize and solve common engineering challenges for deep learning systems
• Understand the deep learning development cycle
• Automate training for models in TensorFlow and PyTorch
• Optimize dataset management, training, model serving and hyperparameter tuning
• Pick the right open-source project for your platform

Deep learning systems are the components and infrastructure essential to supporting a deep learning model in a production environment. Written especially for software engineers with minimal knowledge of deep learning's design requirements, Designing Deep Learning Systems is full of hands-on examples that will help you transfer your software development skills to creating these deep learning platforms. You'll learn how to build automated and scalable services for core tasks like dataset management, model training/serving, and hyperparameter tuning. This book is the perfect way to step into an exciting--and lucrative--career as a deep learning engineer.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the technology
To be practically usable, a deep learning model must be built into a software platform. As a software engineer, you need a deep understanding of deep learning to create such a system. Th is book gives you that depth.

About the book
Designing Deep Learning Systems: A software engineer's guide teaches you everything you need to design and implement a production-ready deep learning platform. First, it presents the big picture of a deep learning system from the developer's perspective, including its major components and how they are connected. Then, it carefully guides you through the engineering methods you'll need to build your own maintainable, efficient, and scalable deep learning platforms.

What's inside

• The deep learning development cycle
• Automate training in TensorFlow and PyTorch
• Dataset management, model serving, and hyperparameter tuning
• A hands-on deep learning lab

About the reader
For software developers and engineering-minded data scientists. Examples in Java and Python.

About the author
Chi Wang is a principal software developer in the Salesforce Einstein group. Donald Szeto was the co-founder and CTO of PredictionIO.

Table of Contents
1 An introduction to deep learning systems
2 Dataset management service
3 Model training service
4 Distributed training
5 Hyperparameter optimization service
6 Model serving design
7 Model serving in practice
8 Metadata and artifact store
9 Workflow orchestration
10 Path to production

一本關於建立將深度學習模型應用於實際環境的平台和系統的重要指南。

摘要
在《設計深度學習系統》中，您將學習如何：
- 將軟體開發技能轉移到深度學習系統上
- 辨識並解決深度學習系統常見的工程挑戰
- 瞭解深度學習開發週期
- 在 TensorFlow 和 PyTorch 中自動化模型訓練
- 優化資料集管理、訓練、模型服務和超參數調整
- 選擇適合您平台的開源專案

深度學習系統是在實際環境中支援深度學習模型所必需的組件和基礎架構。《設計深度學習系統》專為軟體工程師撰寫，並且假設讀者對深度學習的設計需求了解有限。本書充滿了實際範例，將幫助您將軟體開發技能轉移到建立這些深度學習平台上。您將學習如何建立自動化且可擴展的核心任務服務，例如資料集管理、模型訓練/服務和超參數調整。這本書是成為一名深度學習工程師的理想途徑，並且這是一個令人興奮且有利可圖的職業。

購買印刷版書籍將包含 Manning Publications 提供的 PDF、Kindle 和 ePub 格式的免費電子書。

關於技術
要實際使用，深度學習模型必須建立在軟體平台上。作為一名軟體工程師，您需要對深度學習有深入的了解，以創建這樣的系統。本書將提供您這種深度的理解。

關於本書
《設計深度學習系統：軟體工程師的指南》教您設計和實施一個適用於生產環境的深度學習平台所需的一切。首先，它從開發者的角度呈現了深度學習系統的整體架構，包括其主要組件及其連接方式。然後，它會仔細指導您通過工程方法來建立可維護、高效且可擴展的深度學習平台。

內容簡介
- 深度學習開發週期
- 在 TensorFlow 和 PyTorch 中自動化訓練
- 資料集管理、模型服務和超參數調整
- 實踐中的實作深度學習實驗室

讀者對象
軟體開發人員和工程導向的數據科學家。範例使用 Java 和 Python。

關於作者
Chi Wang 是 Salesforce Einstein 團隊的首席軟體開發人員。Donald Szeto 是 PredictionIO 的聯合創始人和首席技術官。

目錄
1 深度學習系統介紹
2 資料集管理服務
3 模型訓練服務
4 分散式訓練
5 超參數優化服務
6 模型服務設計
7 實踐中的模型服務
8 元數據和資源庫
9 工作流程編排
10 通往生產的道路

Chi Wang is a principal software developer in the Salesforce Einstein group where he builds the deep learning platform for millions of Salesforce customers. Previously, he worked at Microsoft Bing and Azure on building large-scale distributed systems. Chi has filed six patents, mostly in deep learning systems.

Donald Szeto was the co-founder and CTO of PredictionIO, a startup that aimed to help democratize and accelerate the adoption of machine learning. PredictionIO was acquired by Salesforce, where he continued his work on machine learning and deep learning systems. Donald is currently investing in, advising, and mentoring technology startups.

Chi Wang是Salesforce Einstein團隊的首席軟體開發人員，他為數百萬Salesforce客戶建立了深度學習平台。在此之前，他曾在Microsoft Bing和Azure工作，建立了大規模分散式系統。Chi已經申請了六項專利，大部分是關於深度學習系統。

Donald Szeto是PredictionIO的聯合創始人和首席技術官，該初創公司旨在幫助普及和加速機器學習的應用。PredictionIO被Salesforce收購後，他繼續在機器學習和深度學習系統上的工作。Donald目前投資、提供諮詢和指導技術初創公司。