Getting Started with Amazon SageMaker Studio

Learn how to build production-grade machine learning models with Amazon SageMaker Studio, the first integrated development environment (IDE) in the cloud, using real-life machine learning examples and code

Key Features

• Understand the machine learning lifecycle in the cloud and its development on Amazon SageMaker Studio
• Master SageMaker Studio components for the machine learning lifecycle and apply SageMaker features in ML use cases
• Scale and operationalize the ML lifecycle effectively using SageMaker Studio

Book Description

Amazon SageMaker Studio is the first integrated development environment for machine learning and is designed to combine data preparation, feature engineering, statistical bias detection, 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 machine learning models in production. Later, you'll understand how to effectively scale and operationalize the machine learning lifecycle using SageMaker Studio.

By the end of this book, you'll have learned machine learning best practices with Amazon SageMaker Studio as well as be able to improve productivity in the machine learning development lifecycle and build and deploy models easily for your ML use cases.

What you will learn

• Explore the machine learning development lifecycle in the cloud
• Understand SageMaker Studio features, components, and the user interface
• Build a dataset with clicks and host a features store for machine learning
• Train machine learning models with ease and efficiency
• Create machine learning models and solutions without writing code
• Host machine learning models in the cloud with efficient cloud infrastructure
• Apply governance, reproducibility, and operational excellence to machine learning projects
• Ensure optimal model performance with model monitoring

Who This Book Is For

This book is for data scientists and machine learning engineers who are looking to get 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.

Table of Contents

1. Machine Learning and Its Life Cycle in the Cloud
2. Introducing Amazon SageMaker Studio
3. Data Preparation with SageMaker Data Wrangler
4. Detecting ML bias with SageMaker Clarify
5. Building a Feature Repository with SageMaker Feature Store
6. Building and Training ML Models with SageMaker Studio IDE
7. Hosting ML Models in the Cloud: Best Practices
8. Jumpstarting ML with SageMaker JumpStart and Autopilot
9. Training ML models at Scale in SageMaker Studio
10. Monitoring ML Models in Production with SageMaker Model Monitoring
11. Operationalizing ML Projects with SageMaker Projects, Pipelines and Model Registry