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.
publication date
2022-03-31
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
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
approach
Walk through end-to-end experience of developing ML models in Amazon SageMaker Studio with real-life examples. In each example, readers get hands-on with accompanying python notebooks and step-by-step explanations of essential concepts and practical recommendations.
audience
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.
short description
Developers working with machine learning will be able to put their knowledge to work with this practical guide to Amazon SageMaker Studio. The book takes a hands-on approach to implementing real-world machine learning use cases that will have you up and running quickly.
subtitle
Learn to build end-to-end machine learning projects in the SageMaker machine learning IDE
keywords
AWS Sagemaker, AWS Machine Learning, Machine Learning, Sagemaker Studio
Product ISBN
9781801070157
