Getting Started with Amazon SageMaker Studio Book [PDF] Download

Download the fantastic book titled Getting Started with Amazon SageMaker Studio written by Michael Hsieh, available in its entirety in both PDF and EPUB formats for online reading. This page includes a concise summary, a preview of the book cover, and detailed information about "Getting Started with Amazon SageMaker Studio", which was released on 31 March 2022. We suggest perusing the summary before initiating your download. This book is a top selection for enthusiasts of the Computers genre.

Summary of Getting Started with Amazon SageMaker Studio by Michael Hsieh PDF

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 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 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 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.


Detail About Getting Started with Amazon SageMaker Studio PDF

  • Author : Michael Hsieh
  • Publisher : Packt Publishing Ltd
  • Genre : Computers
  • Total Pages : 327 pages
  • ISBN : 1801073481
  • PDF File Size : 34,6 Mb
  • Language : English
  • Rating : 4/5 from 21 reviews

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Getting Started with Amazon SageMaker Studio

Getting Started with Amazon SageMaker Studio
  • Publisher : Packt Publishing Ltd
  • File Size : 42,6 Mb
  • Release Date : 31 March 2022
GET BOOK

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

Getting Started with Amazon SageMaker Studio

Getting Started with Amazon SageMaker Studio
  • Publisher : Packt Publishing Ltd
  • File Size : 21,8 Mb
  • Release Date : 31 March 2022
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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

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Computer Vision on AWS
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  • File Size : 39,7 Mb
  • Release Date : 31 March 2023
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Develop scalable computer vision solutions for real-world business problems and discover scaling, cost reduction, security, and bias mitigation best practices with AWS AI/ML services Purchase of the print or

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Geospatial Data Analytics on AWS
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  • File Size : 21,8 Mb
  • Release Date : 30 June 2023
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Build an end-to-end geospatial data lake in AWS using popular AWS services such as RDS, Redshift, DynamoDB, and Athena to manage geodata Purchase of the print or Kindle book includes

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  • Publisher : Packt Publishing Ltd
  • File Size : 27,6 Mb
  • Release Date : 28 October 2022
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Plan and design model serving infrastructure to run and troubleshoot distributed deep learning training jobs for improved model performance. Key FeaturesExplore key Amazon SageMaker capabilities in the context of deep

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  • Publisher : Packt Publishing Ltd
  • File Size : 25,8 Mb
  • Release Date : 30 August 2023
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Supercharge and deploy Amazon Redshift Serverless, train and deploy machine learning models using Amazon Redshift ML, and run inference queries at scale Key Features Leverage supervised learning to build binary

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  • Publisher : Packt Publishing Ltd
  • File Size : 38,5 Mb
  • Release Date : 29 October 2021
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A step-by-step solution-based guide to preparing building, training, and deploying high-quality machine learning models with Amazon SageMaker Key FeaturesPerform ML experiments with built-in and custom algorithms in SageMakerExplore proven solutions

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  • Publisher : Packt Publishing Ltd
  • File Size : 51,8 Mb
  • Release Date : 18 February 2021
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Get to grips with automated machine learning and adopt a hands-on approach to AutoML implementation and associated methodologies Key FeaturesGet up to speed with AutoML using OSS, Azure, AWS, GCP,

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  • Publisher : "O'Reilly Media, Inc."
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Amazon Redshift powers analytic cloud data warehouses worldwide, from startups to some of the largest enterprise data warehouses available today. This practical guide thoroughly examines this managed service and demonstrates

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  • Publisher : Packt Publishing Ltd
  • File Size : 35,5 Mb
  • Release Date : 27 August 2020
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Quickly build and deploy machine learning models without managing infrastructure, and improve productivity using Amazon SageMaker’s capabilities such as Amazon SageMaker Studio, Autopilot, Experiments, Debugger, and Model Monitor Key