Data Science on AWS
- Downloads：2803
- Type：Epub+TxT+PDF+Mobi
- Create Date：2021-06-01 10:51:04
- Update Date：2025-09-06
- Status：finish
- Author：Chris Fregly
- ISBN：1492079391
- Environment：PC/Android/iPhone/iPad/Kindle
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Summary
If you use data to make critical business decisions， this book is for you。 Whether you’re a data analyst， research scientist， data engineer， ML engineer， data scientist， application developer， or systems developer， this guide helps you broaden your understanding of the modern data science stack， create your own machine learning pipelines， and deploy them to applications at production scale。
The AWS data science stack unifies data science， data engineering， and application development to help you level up your skills beyond your current role。 Authors Antje Barth and Chris Fregly show you how to build your own ML pipelines from existing APIs， submit them to the cloud， and integrate results into your application in minutes instead of days。
The AWS data science stack unifies data science， data engineering， and application development to help you level up your skills beyond your current role。 Authors Antje Barth and Chris Fregly show you how to build your own ML pipelines from existing APIs， submit them to the cloud， and integrate results into your application in minutes instead of days。
Reviews
Sebastian Gebski
，
It simply HAD to be a success: super-interesting topic （data science）， amazing tech （SageMaker）， the author who's a real expert on the topic， published who puts a lot of effort in keeping the bar high， 。。。But it isn't （a success）。 I so wanted to fall in love with this book， but I didn't。 Why？1。 The author really wanted to show as much Data Science goodness as possible。 Unfortunately， as a result， the whole book feels like a roller-coaster or US tourists' trip across Europe （"10 capital cities in It simply HAD to be a success: super-interesting topic （data science）， amazing tech （SageMaker）， the author who's a real expert on the topic， published who puts a lot of effort in keeping the bar high， 。。。But it isn't （a success）。 I so wanted to fall in love with this book， but I didn't。 Why？1。 The author really wanted to show as much Data Science goodness as possible。 Unfortunately， as a result， the whole book feels like a roller-coaster or US tourists' trip across Europe （"10 capital cities in 2 weeks"）。 Zillion topics， but each of them covered in a rush， w/o setting proper foundations。 Don't get me wrong， he does keep the proper structure - the chapters do have introductions， but after a good start （initial 2 pages of general context）， he dives super deep into Python code 。。。2。 SageMaker is not a trivial service。 There are some unobvious conceptual constructs that require careful elaboration。 It's hard to navigate across such a complex service w/o a proper overview， possibly some examples that approach it from very different angles。 Or a real-life study across the whole model's lifecycle。 I think that's what is totally missing in official AWS docs and 。。。 unfortunately， it's also what's missing in the book。 YES， there are very specific code samples that I assume are correct and solve a narrow， particular problem。 But does reading through them make the reader able to compose a solution to another （even similar） problem？ I don't think so 。。。3。 I was wondering - who could be the best audience for this book。 And I have a surprising conclusion: probably people who already know the SageMaker basics， who have used it in a single scenario or two。 This book 'drafts' briefly so many scenarios that can spur their imagination （regarding what's possible in AWS when it comes to AI/ML） and they already have enough understanding of concepts behind the service to take it from here。It really saddens me to rate this book so harshly， especially because it's very visible how much effort was put into it。 But I can't recommend it to people who'd like to learn how to do Data Science on AWS :（ Because they won't。 I wouldn't be able to。2。5-2。7 stars 。。。more
Xianshun Chen
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Pretty good on the part such as how to use SageMaker Studio and AutoPilot as well as Athena The later chapters on BERT and tensorflow is not easy to follow as the book is still in its early release。 Also the SageMaker's ScriptProcessor to run Spark jobs need more effort to make the examples runnable。 Pretty good on the part such as how to use SageMaker Studio and AutoPilot as well as Athena The later chapters on BERT and tensorflow is not easy to follow as the book is still in its early release。 Also the SageMaker's ScriptProcessor to run Spark jobs need more effort to make the examples runnable。 。。。more
Reference
google book https://www.google.com/search?tbm=bks&q=Data Science on AWS
hathitrust digital library https://babel.hathitrust.org/cgi/ls?q1=Data Science on AWS&field1=ocr&a=srchls&ft=ft&lmt=ft
The British Library https://bll01.primo.exlibrisgroup.com/discovery/search?query=any,contains,Data Science on AWS&tab=LibraryCatalog&search_scope=Not_BL_Suppress&vid=44BL_INST:BLL01&lang=en&offset=0