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Getting Started with Amazon SageMaker Studio: Learn to build end-to-end machine learning projects in the SageMaker machine learning IDE
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 FeaturesUnderstand the ML lifecycle in the cloud and its development on Amazon SageMaker StudioLearn to apply SageMaker features in SageMaker Studio for ML use casesScale and operationalize the ML lifecycle effectively using SageMaker StudioBook DescriptionAmazon SageMaker Studio is the first integrated development environment (IDE) for machine learning (ML) and is designed to integrate ML 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 learnExplore the ML development life cycle in the cloudUnderstand SageMaker Studio features and the user interfaceBuild a dataset with clicks and host a feature store for MLTrain ML models with ease and scaleCreate ML models and solutions with little codeHost ML models in the cloud with optimal cloud resourcesEnsure optimal model performance with model monitoringApply governance and operational excellence to ML projectsWho this book is forThis 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 ContentsMachine Learning and Its Life Cycle in the CloudIntroducing Amazon SageMaker StudioData Preparation with SageMaker Data WranglerBuilding a Feature Repository with SageMaker Feature StoreBuilding and Training ML Models with SageMaker Studio IDEDetecting ML Bias and Explaining Models with SageMaker ClarifyHosting ML Models in the Best PracticesJumpstarting ML with SageMaker JumpStart and AutopilotTraining ML Models at Scale in SageMaker StudioMonitoring ML Models in Production with SageMaker Model MonitorOperationalize ML Projects with SageMaker Projects, Pipelines and Model Registry
Key FeaturesUnderstand the ML lifecycle in the cloud and its development on Amazon SageMaker StudioLearn to apply SageMaker features in SageMaker Studio for ML use casesScale and operationalize the ML lifecycle effectively using SageMaker StudioBook DescriptionAmazon SageMaker Studio is the first integrated development environment (IDE) for machine learning (ML) and is designed to integrate ML 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 learnExplore the ML development life cycle in the cloudUnderstand SageMaker Studio features and the user interfaceBuild a dataset with clicks and host a feature store for MLTrain ML models with ease and scaleCreate ML models and solutions with little codeHost ML models in the cloud with optimal cloud resourcesEnsure optimal model performance with model monitoringApply governance and operational excellence to ML projectsWho this book is forThis 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 ContentsMachine Learning and Its Life Cycle in the CloudIntroducing Amazon SageMaker StudioData Preparation with SageMaker Data WranglerBuilding a Feature Repository with SageMaker Feature StoreBuilding and Training ML Models with SageMaker Studio IDEDetecting ML Bias and Explaining Models with SageMaker ClarifyHosting ML Models in the Best PracticesJumpstarting ML with SageMaker JumpStart and AutopilotTraining ML Models at Scale in SageMaker StudioMonitoring ML Models in Production with SageMaker Model MonitorOperationalize ML Projects with SageMaker Projects, Pipelines and Model Registry
326 pages, Kindle Edition
Published March 31, 2022
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