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Amazon SageMaker Best Practices: Proven tips and tricks to build successful machine learning solutions on Amazon SageMaker - Softcover
- VerlagPackt Publishing
- Erscheinungsdatum2021
- ISBN 10 1801070520
- ISBN 13 9781801070522
- EinbandTapa blanda
- SpracheEnglisch
- Anzahl der Seiten348
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Amazon SageMaker Best Practices: Proven tips and tricks to build successful machine learning solutions on Amazon SageMaker (Paperback or Softback)
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Zustand: New. Amazon SageMaker Best Practices: Proven tips and tricks to build successful machine learning solutions on Amazon SageMaker (Paperback or Softback) 1.32. Bestandsnummer des Verkäufers BBS-9781801070522
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Amazon SageMaker Best Practices: Proven tips and tricks to build successful machine learning solutions on Amazon SageMaker
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Amazon SageMaker Best Practices
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Amazon SageMaker Best Practices: Proven tips and tricks to build successful machine learning solutions on Amazon SageMaker
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Amazon SageMaker Best Practices: Proven tips and tricks to build successful machine learning solutions on Amazon SageMaker
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Packt Publishing - ebooks Account, 2021
ISBN 10: 1801070520
ISBN 13: 9781801070522
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Amazon SageMaker Best Practices: Proven tips and tricks to build successful machine learning solutions on Amazon SageMaker
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ISBN 13: 9781801070522
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Amazon SageMaker Best Practices : Proven tips and tricks to build successful machine learning solutions on Amazon SageMaker
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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 Description: Amazon 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 Not Elektronisches Buch and Python is expected. Basic knowledge of AWS related to data, security, and monitoring will help you make the most of the book. Bestandsnummer des Verkäufers 9781801070522
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Amazon SageMaker Best Practices
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Zustand: New. Going beyond the basics, Amazon SageMaker Best Practices provides end-to-end coverage of the service capabilities that the platform offers for building and automating machine learning workloads to address data science challenges. With this book, you ll disc. Bestandsnummer des Verkäufers 532754881
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