Packt - Amazon Sagemaker Best Practices Proven Tips And Tricks To Build Successful Machine Learni...

  Amazon SageMaker Best Practices - Proven Tips And Tricks To Build Successful
  Machine Learning Solutions On Amazon SageMaker
   LANGUAGE: English     ░   ISBN: 9781801070522      ░   REMOVED: DRM
https://www.worldcat.org/search?q=bn%3A9781801070522&fq=x0%3Abook+%2B+x4%3Adigit
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  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 Notebooks and Python is expected.
 Basic knowledge of AWS related to data, security, and monitoring will help
 you make the most of the book.

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