• 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 this book is for ?

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

Part 1 – Introduction to Machine Learning on Amazon SageMaker Studio

• In this section, we will cover an introduction to machine learning (ML), the ML life cycle in the cloud, and Amazon SageMaker Studio. This section also includes a level set on the domain terminology in ML with example use cases.

This section comprises the following chapters: • Chapter 1, Machine Learning and Its Life Cycle in the Cloud • Chapter 2, Introducing Amazon SageMaker Studio

Part 2 – End-to-End Machine Learning Life Cycle with SageMaker Studio

• In this section of the book, you will gain a working knowledge of each SageMaker Studio component for the machine learning (ML) life cycle and how and when to apply SageMaker features in your ML use cases.

This section comprises the following chapters: • Chapter 3, Data Preparation with SageMaker Data Wrangler • Chapter 4, Building a Feature Repository with SageMaker Feature Store • Chapter 5, Building and Training ML Models with SageMaker Studio IDE • Chapter 6, Detecting ML Bias and Explaining Models with SageMaker Clarify • Chapter 7, Hosting ML Models in the Cloud: Best Practices • Chapter 8, Jumpstarting ML with SageMaker JumpStart and Autopilot

Part 3 – The Production and Operation of Machine Learning with SageMaker Studio

• In this section, you will learn how to effectively scale and operationalize the machine learning (ML) life cycle using SageMaker Studio so that you can reduce the amount of manual and undifferentiating work needed from a data scientist and allow them to focus on modeling.

This section comprises the following chapters: • Chapter 9, Training ML Models at Scale in SageMaker Studio • Chapter 10, Monitoring ML Models in Production with SageMaker Model Monitoring • Chapter 11, Operationalize ML Projects with SageMaker Projects, Pipelines, and Model Registry

Conclusion

• I thoroughly enjoyed reading "Getting Started with Amazon SageMaker Studio" by Michael Hsieh. Thank you to Shifa Ansari at Packt team for sharing this book and for the opportunity of an Editorial Review of the same. Strongly recommend it if you’re Learning to build end-to-end machine learning projects in the SageMaker machine learning IDE and are looking to get started with Amazon Sagemaker Studio.

Personally, I loved 👀👇:

👉 The book starts by exploring the features available in Amazon SageMaker Studio to analyze data, develop ML models, and productionize models to meet your goals.

👉 the book does a good job of explaining how these features work together to address common challenges when building ML models in production.

👉 Finally it helps us understand how to effectively scale and operationalize the ML life cycle using SageMaker Studio

👉 Link of the Book is here

This post is a collaboration with Packt, I recommend following them if you are interested in book releases and growing the community! ❤