Distributed Data Systems with Azure Databricks
內容簡介
Quickly build and deploy massive data pipelines and improve productivity using Azure Databricks
Key Features: Get to grips with the distributed training and deployment of machine learning and deep learning modelsLearn how ETLs are integrated with Azure Data Factory and Delta LakeExplore deep learning and machine learning models in a distributed computing infrastructure
Book Description:
Microsoft Azure Databricks helps you to harness the power of distributed computing and apply it to create robust data pipelines, along with training and deploying machine learning and deep learning models. Databricks' advanced features enable developers to process, transform, and explore data. Distributed Data Systems with Azure Databricks will help you to put your knowledge of Databricks to work to create big data pipelines.
The book provides a hands-on approach to implementing Azure Databricks and its associated methodologies that will make you productive in no time. Complete with detailed explanations of essential concepts, practical examples, and self-assessment questions, you'll begin with a quick introduction to Databricks core functionalities, before performing distributed model training and inference using TensorFlow and Spark MLlib. As you advance, you'll explore MLflow Model Serving on Azure Databricks and implement distributed training pipelines using HorovodRunner in Databricks.
Finally, you'll discover how to transform, use, and obtain insights from massive amounts of data to train predictive models and create entire fully working data pipelines. By the end of this MS Azure book, you'll have gained a solid understanding of how to work with Databricks to create and manage an entire big data pipeline.
What You Will Learn: Create ETLs for big data in Azure DatabricksTrain, manage, and deploy machine learning and deep learning modelsIntegrate Databricks with Azure Data Factory for extract, transform, load (ETL) pipeline creationDiscover how to use Horovod for distributed deep learningFind out how to use Delta Engine to query and process data from Delta LakeUnderstand how to use Data Factory in combination with DatabricksUse Structured Streaming in a production-like environment
Who this book is for:
This book is for software engineers, machine learning engineers, data scientists, and data engineers who are new to Azure Databricks and want to build high-quality data pipelines without worrying about infrastructure. Knowledge of Azure Databricks basics is required to learn the concepts covered in this book more effectively. A basic understanding of machine learning concepts and beginner-level Python programming knowledge is also recommended.
配送方式
-
台灣
- 國內宅配:本島、離島
-
到店取貨:
不限金額免運費
-
海外
- 國際快遞:全球
-
港澳店取:
訂購/退換貨須知
加入金石堂 LINE 官方帳號『完成綁定』,隨時掌握出貨動態:
商品運送說明:
- 本公司所提供的產品配送區域範圍目前僅限台灣本島。注意!收件地址請勿為郵政信箱。
- 商品將由廠商透過貨運或是郵局寄送。消費者訂購之商品若無法送達,經電話或 E-mail無法聯繫逾三天者,本公司將取消該筆訂單,並且全額退款。
- 當廠商出貨後,您會收到E-mail出貨通知,您也可透過【訂單查詢】確認出貨情況。
- 產品顏色可能會因網頁呈現與拍攝關係產生色差,圖片僅供參考,商品依實際供貨樣式為準。
- 如果是大型商品(如:傢俱、床墊、家電、運動器材等)及需安裝商品,請依商品頁面說明為主。訂單完成收款確認後,出貨廠商將會和您聯繫確認相關配送等細節。
- 偏遠地區、樓層費及其它加價費用,皆由廠商於約定配送時一併告知,廠商將保留出貨與否的權利。
提醒您!!
金石堂及銀行均不會請您操作ATM! 如接獲電話要求您前往ATM提款機,請不要聽從指示,以免受騙上當!
退換貨須知:
**提醒您,鑑賞期不等於試用期,退回商品須為全新狀態**
-
依據「消費者保護法」第19條及行政院消費者保護處公告之「通訊交易解除權合理例外情事適用準則」,以下商品購買後,除商品本身有瑕疵外,將不提供7天的猶豫期:
- 易於腐敗、保存期限較短或解約時即將逾期。(如:生鮮食品)
- 依消費者要求所為之客製化給付。(客製化商品)
- 報紙、期刊或雜誌。(含MOOK、外文雜誌)
- 經消費者拆封之影音商品或電腦軟體。
- 非以有形媒介提供之數位內容或一經提供即為完成之線上服務,經消費者事先同意始提供。(如:電子書、電子雜誌、下載版軟體、虛擬商品…等)
- 已拆封之個人衛生用品。(如:內衣褲、刮鬍刀、除毛刀…等)
- 若非上列種類商品,均享有到貨7天的猶豫期(含例假日)。
- 辦理退換貨時,商品(組合商品恕無法接受單獨退貨)必須是您收到商品時的原始狀態(包含商品本體、配件、贈品、保證書、所有附隨資料文件及原廠內外包裝…等),請勿直接使用原廠包裝寄送,或於原廠包裝上黏貼紙張或書寫文字。
- 退回商品若無法回復原狀,將請您負擔回復原狀所需費用,嚴重時將影響您的退貨權益。




商品評價