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Getting Started with Amazon SageMaker Studio
Getting Started with Amazon SageMaker Studio

Getting Started with Amazon SageMaker Studio: Learn to build end-to-end machine learning projects in the SageMaker machine learning IDE

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Getting Started with Amazon SageMaker Studio

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Key benefits

  • Understand the ML lifecycle in the cloud and its development on Amazon SageMaker Studio
  • Learn to apply SageMaker features in SageMaker Studio for ML use cases
  • Scale and operationalize the ML lifecycle effectively using SageMaker Studio

Description

Amazon SageMaker Studio is the first integrated development environment (IDE) for machine learning (ML) and is designed to integrate ML workflows: data preparation, feature engineering, statistical bias detection, automated machine learning (AutoML), training, hosting, ML explainability, monitoring, and MLOps in one environment. In this book, you'll start by exploring the features available in Amazon SageMaker Studio to analyze data, develop ML models, and productionize models to meet your goals. As you progress, you will learn how these features work together to address common challenges when building ML models in production. After that, you'll understand how to effectively scale and operationalize the ML life cycle using SageMaker Studio. By the end of this book, you'll have learned ML best practices regarding Amazon SageMaker Studio, as well as being able to improve productivity in the ML development life cycle and build and deploy models easily for your ML use cases.

Who is this book for?

This book is for data scientists and machine learning engineers who are looking to become well-versed with Amazon SageMaker Studio and gain hands-on machine learning experience to handle every step in the ML lifecycle, including building data as well as training and hosting models. Although basic knowledge of machine learning and data science is necessary, no previous knowledge of SageMaker Studio and cloud experience is required.

What you will learn

  • Explore the ML development life cycle in the cloud
  • Understand SageMaker Studio features and the user interface
  • Build a dataset with clicks and host a feature store for ML
  • Train ML models with ease and scale
  • Create ML models and solutions with little code
  • Host ML models in the cloud with optimal cloud resources
  • Ensure optimal model performance with model monitoring
  • Apply governance and operational excellence to ML projects
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Publication date, Length, Edition, Language, ISBN-13
Publication date : Mar 31, 2022
Length: 326 pages
Edition : 1st
Language : English
ISBN-13 : 9781801070157
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Length: 326 pages
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Language : English
ISBN-13 : 9781801070157
Category :
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Table of Contents

15 Chapters
Part 1 – Introduction to Machine Learning on Amazon SageMaker Studio Chevron down icon Chevron up icon
Chapter 1: Machine Learning and Its Life Cycle in the Cloud Chevron down icon Chevron up icon
Chapter 2: Introducing Amazon SageMaker Studio Chevron down icon Chevron up icon
Part 2 – End-to-End Machine Learning Life Cycle with SageMaker Studio Chevron down icon Chevron up icon
Chapter 3: Data Preparation with SageMaker Data Wrangler Chevron down icon Chevron up icon
Chapter 4: Building a Feature Repository with SageMaker Feature Store Chevron down icon Chevron up icon
Chapter 5: Building and Training ML Models with SageMaker Studio IDE Chevron down icon Chevron up icon
Chapter 6: Detecting ML Bias and Explaining Models with SageMaker Clarify Chevron down icon Chevron up icon
Chapter 7: Hosting ML Models in the Cloud: Best Practices Chevron down icon Chevron up icon
Chapter 8: Jumpstarting ML with SageMaker JumpStart and Autopilot Chevron down icon Chevron up icon
Part 3 – The Production and Operation of Machine Learning with SageMaker Studio Chevron down icon Chevron up icon
Chapter 9: Training ML Models at Scale in SageMaker Studio Chevron down icon Chevron up icon
Chapter 10: Monitoring ML Models in Production with SageMaker Model Monitor Chevron down icon Chevron up icon
Chapter 11: Operationalize ML Projects with SageMaker Projects, Pipelines, and Model Registry Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8
(13 Ratings)
5 star 84.6%
4 star 15.4%
3 star 0%
2 star 0%
1 star 0%
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C. C Chin Jul 07, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Is it possible AWS Sagemaker studio to follow Chris and Prof Chip books, grad class book for AWS mls-c01 examKill two birds, also Dr Song book tooGot book start reading!!Need for job interview and newbie AWS mls-c01 machine learning specalty exam and sagemaker Studio!!AWS MLS; guess need the best..One reviewer OM S says book good for AWS MLS-C01 machine learning specalty exam!! We shall see.Also MLS sybex book too!!Neal Davis MLS exams!! 120 question,course and 6 projects udemy.OmS says for beginner like me open free acct and have $budget $25. Then do beginner chapter 1-11 !!Pass AWS mls-c01!! Passed DBS barely!!Perfect!! 👍Using udemy course sagemaker and MLS-c01Learn ML jargon, other book too!!Dr Logan Song ch 5,6 Ai chapters hands on lab with introduction material on Das, and MLS basics!!Fed government job interview!!Also need to pass MLS-c01 or c02 2026??And DEA-c01 is out too!!Sybex book n practice exams mls-c01Also Julian SimonLearn Amazon Sagemaker 2nd edition2021We shall see!!
Amazon Verified review Amazon
Roger May 10, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
A very comprehensive book, ranging from the basics like the ML life cycle to end-to-end workflow using the SageMaker Studio. For me, the chapter covering ML bias and model explainability with SageMaker Clarify was really helpful to detect data drift in my projects. Can be a great resource to both novices and experienced ML practitioners wishing to get started in the AWS ML services.
Amazon Verified review Amazon
Yiqiao Yin Jul 06, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The first interesting point is the usage of AWS Sagemaker. It is one of the most popular AWS services and it’s been spreading like crazy after its inauguration. The past five years AWS has really had a lot of features inside Sagemaker when they introduced the studio capability all in one place. This is really the platform they provide you not just the robust service but also the most complete features amongst all cloud platforms. The author really walks through the content from the needs for the beginners and the first time readers what is book is about an author is able to really unpack the complicated documents in easy steps for us. I will recommend for the new data scientist who have basic understanding of cloud service to use this book to learn more in-depth knowledge about machine learning operations and cloud platforms. For advanced scientists this book is also recommended as a popular guidance for some of the most advanced features such as data preparation, feature engineer, automated machine learning, and MLOps.I would highly recommend this book to other people!
Amazon Verified review Amazon
Nick Minaie Apr 25, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
"5-star" is what comes to mind when reading this book! I truly enjoyed reading this book and learned a lot about developing end-2-end ML solutions with Amazon SageMaker. This is a fantastic and comprehensive reference book for every ML practitioner and Data Scientist for everything SageMaker, from A to Z, end-to-end.Even though I had experience with SageMaker, after reviewing the book I realized how much I didn't know about its features and capabilities.More specifically, I learned a lot about Experiments and also Pipelines that are critical for modern production level ML solutions. Feature Store is a new addition to SageMaker and the book does a great job in walking the reader through different type of FS and how to leverage them in ML solutions.Detecting bias and avoiding bias in ML solutions are highly important in the industry, and "Detecting ML Bias and Explaining Models with SageMaker Clarify" chapter provides a detailed overview of Clarify feature in Amazon SageMaker Studio, which I am bookmarking for future reference.JumpStart is great collection of samples and notebooks that are readily available and can be used by developers to jump start their ML projects. I enjoyed reading this section and learning how to leverage this for my projects.I highly recommend this book to all developers who want to learn about Amazon SageMaker ML platform, or to have a comprehensive reference guide for the platform that will be handy in every step of the project.
Amazon Verified review Amazon
Om S Apr 11, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Sagemaker has been one of the AWS services which spread like wildfire since its inauguration.Over 5 years AWS has added many features in SageMaker since they introduce studio capability in one place which makes this service one of the best and outstanding among other cloud platforms.POC of a model to deployment in a product has never been easy. In between, there are so many challenges.This book brings all those ML connecting bits and pieces and allows a novice data scientist who has a basic understanding of AWS core services to deploy solutions in production without much difficulty.This book covers all accept of workflow from data preparation, Feature engineering AutoML, model training, monitoring, and MLOps all in one place at last but not the least making easy to productization of models for any complex business needs which makes this book attractive.All the best practices are given in consequent chapters.This is a very practical book in order to take maximum advantage open a free AWS account and start exploring Sagemaker Studio with the help of this book chapter after chapter at the end you will be happy. Not only that you will be very confident to pass the AWS ML specialty certification exam.
Amazon Verified review Amazon
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