| Description |
1 online resource (348 pages) |
| Note |
Description based upon print version of record. |
| Contents |
Cover -- Title Page -- Copyright and Credits -- Dedication -- Contributors -- Table of Contents -- Preface -- Section 1: Processing Data at Scale -- Chapter 1: Amazon SageMaker Overview -- Technical requirements -- Preparing, building, training and tuning, deploying, and managing ML models -- Discussion of data preparation capabilities -- SageMaker Ground Truth -- SageMaker Data Wrangler -- SageMaker Processing -- SageMaker Feature Store -- SageMaker Clarify -- Feature tour of model-building capabilities -- SageMaker Studio -- SageMaker notebook instances -- SageMaker algorithms |
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BYO algorithms and scripts -- Feature tour of training and tuning capabilities -- SageMaker training jobs -- Autopilot -- HPO -- SageMaker Debugger -- SageMaker Experiments -- Feature tour of model management and deployment capabilities -- Model Monitor -- Model endpoints -- Edge Manager -- Summary -- Chapter 2: Data Science Environments -- Technical requirements -- Machine learning use case and dataset -- Creating data science environments -- Creating repeatability through IaC/CaC -- Amazon SageMaker notebook instances -- Amazon SageMaker Studio |
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Providing and creating data science environments as IT services -- Creating a portfolio in AWS Service Catalog -- Amazon SageMaker notebook instances -- Amazon SageMaker Studio -- Summary -- References -- Chapter 3: Data Labeling with Amazon SageMaker Ground Truth -- Technical requirements -- Challenges with labeling data at scale -- Addressing unique labeling requirements with custom labeling workflows -- A private labeling workforce -- Listing the data to label -- Creating the workflow -- Improving labeling quality using multiple workers -- Using active learning to reduce labeling time |
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Security and permissions -- Summary -- Chapter 4: Data Preparation at Scale Using Amazon SageMaker Data Wrangler and Processing -- Technical requirements -- Visual data preparation with Data Wrangler -- Bias detection and explainability with Data Wrangler and Clarify -- Data preparation at scale with SageMaker Processing -- Loading the dataset -- Drop columns -- Converting data types -- Scaling numeric fields -- Featurizing the date -- Simulating labels for air quality -- Encoding categorical variables -- Splitting and saving the dataset -- Summary |
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Chapter 5: Centralized Feature Repository with Amazon SageMaker Feature Store -- Technical requirements -- Amazon SageMaker Feature Store essentials -- Creating feature groups -- Populating feature groups -- Retrieving features from feature groups -- Creating reusable features to reduce feature inconsistencies and inference latency -- Designing solutions for near real-time ML predictions -- Summary -- References -- Section 2: Model Training Challenges -- Chapter 6: Training and Tuning at Scale -- Technical requirements -- ML training at scale with SageMaker distributed libraries |
| Note |
Choosing between data and model parallelism. |
| Summary |
Overcome advanced challenges in building end-to-end ML solutions by leveraging the capabilities of Amazon SageMaker for developing and integrating ML models into production Key Features Learn best practices for all phases of building machine learning solutions - from data preparation to monitoring models in production Automate end-to-end machine learning workflows with Amazon SageMaker and related AWS Design, architect, and operate machine learning workloads in the AWS Cloud Book DescriptionAmazon SageMaker is a fully managed AWS service that provides the ability to build, train, deploy, and monitor machine learning models. The book begins with a high-level overview of Amazon SageMaker capabilities that map to the various phases of the machine learning process to help set the right foundation. You'll learn efficient tactics to address data science challenges such as processing data at scale, data preparation, connecting to big data pipelines, identifying data bias, running A/B tests, and model explainability using Amazon SageMaker. As you advance, you'll understand how you can tackle the challenge of training at scale, including how to use large data sets while saving costs, monitoring training resources to identify bottlenecks, speeding up long training jobs, and tracking multiple models trained for a common goal. Moving ahead, you'll find out how you can integrate Amazon SageMaker with other AWS to build reliable, cost-optimized, and automated machine learning applications. In addition to this, you'll build ML pipelines integrated with MLOps principles and apply best practices to build secure and performant solutions. By the end of the book, you'll confidently be able to apply Amazon SageMaker's wide range of capabilities to the full spectrum of machine learning workflows. What you will learn Perform data bias detection with AWS Data Wrangler and SageMaker Clarify Speed up data processing with SageMaker Feature Store Overcome labeling bias with SageMaker Ground Truth Improve training time with the monitoring and profiling capabilities of SageMaker Debugger Address the challenge of model deployment automation with CI/CD using the SageMaker model registry Explore SageMaker Neo for model optimization Implement data and model quality monitoring with Amazon Model Monitor Improve training time and reduce costs with SageMaker data and model parallelism Who this book is for This book is for expert data scientists responsible for building machine learning applications using Amazon SageMaker. Working knowledge of Amazon SageMaker, machine learning, deep learning, and experience using Jupyter Notebooks and Python is expected. Basic knowledge of AWS related to data, security, and monitoring will help you make the most of the book. |
| Subject |
Amazon Web Services (Firm)
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Amazon Web Services (Firm) |
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Machine learning.
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Cloud computing.
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Apprentissage automatique. |
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Infonuagique. |
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Cloud computing |
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Machine learning |
| Added Author |
DeFauw, Randy.
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Eigenbrode, Shelbee.
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| Other Form: |
Print version: Muppala, Sireesha Amazon SageMaker Best Practices Birmingham : Packt Publishing, Limited,c2021 |
| ISBN |
9781801077767 (electronic bk.) |
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1801077762 (electronic bk.) |
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