Build production-grade machine learning models with Amazon SageMaker Studio, the first integrated development environment in the cloud, using real-life machine learning examples and code
Key Features
- 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
Book 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.
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
Who this book is 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.
Table of Contents
- Machine Learning and Its Life Cycle in the Cloud
- Introducing Amazon SageMaker Studio
- Data Preparation with SageMaker Data Wrangler
- Building a Feature Repository with SageMaker Feature Store
- Building and Training ML Models with SageMaker Studio IDE
- Detecting ML Bias and Explaining Models with SageMaker Clarify
- Hosting ML Models in the Cloud: Best Practices
- Jumpstarting ML with SageMaker JumpStart and Autopilot
- Training ML Models at Scale in SageMaker Studio
- Monitoring ML Models in Production with SageMaker Model Monitor
- Operationalize ML Projects with SageMaker Projects, Pipelines and Model Registry
Michael Hsieh is a senior AI/machine learning (ML) solutions architect at Amazon Web Services. He creates and evangelizes for ML solutions centered around Amazon SageMaker. He also works with enterprise customers to advance their ML journeys.
Prior to working at AWS, Michael was an advanced analytic consultant creating ML solutions and enterprise-level ML strategies at Slalom Consulting in Philadelphia, PA. Prior to consulting, he was a data scientist at the University of Pennsylvania Health System, focusing on personalized medicine and ML research.
Michael has two master's degrees, one in applied physics and one in robotics.
Originally from Taipei, Taiwan, Michael currently lives in Sammamish, WA, but still roots for the Philadelphia Eagles.
