Getting Started with Amazon SageMaker Studio: Learn to build end-to-end machine learning projects in the SageMaker machine learning IDE
Author: Michael Hsieh (Author)
Publisher Finelybook 出版社：Packt Publishing (March 31, 2022)
pages 页数：326 pages
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
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
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 Author: 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.
Author: 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