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  • Book Overview & Buying AWS Certified Machine Learning - Specialty (MLS-C01) Certification Guide
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AWS Certified Machine Learning - Specialty (MLS-C01) Certification Guide

AWS Certified Machine Learning - Specialty (MLS-C01) Certification Guide - Second Edition

By : Somanath Nanda, Weslley Moura
4.6 (22)
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AWS Certified Machine Learning - Specialty (MLS-C01) Certification Guide

AWS Certified Machine Learning - Specialty (MLS-C01) Certification Guide

4.6 (22)
By: Somanath Nanda, Weslley Moura

Overview of this book

The AWS Certified Machine Learning Specialty (MLS-C01) exam evaluates your ability to execute machine learning tasks on AWS infrastructure. This comprehensive book aligns with the latest exam syllabus, offering practical examples to support your real-world machine learning projects on AWS. Additionally, you'll get lifetime access to supplementary online resources, including mock exams with exam-like timers, detailed solutions, interactive flashcards, and invaluable exam tips, all accessible across various devices—PCs, tablets, and smartphones. Throughout the book, you’ll learn data preparation techniques for machine learning, covering diverse methods for data manipulation and transformation across different variable types. Addressing challenges such as missing data and outliers, the book guides you through an array of machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, text mining, and image processing, accompanied by requisite machine learning algorithms essential for exam success. The book helps you master the deployment of models in production environments and their subsequent monitoring. Equipped with insights from this book and the accompanying mock exams, you'll be fully prepared to achieve the AWS MLS-C01 certification.
Table of Contents (13 chapters)
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Model tuning

In Chapter 7, Evaluating and Optimizing Models, you learned many important concepts about model tuning. Let’s now explore this topic from a practical perspective.

In order to tune a model on SageMaker, you have to call create_hyper_parameter_tuning_job and pass the following main parameters:

  • HyperParameterTuningJobName: This is the name of the tuning job. It is useful to track the training jobs that have been started on behalf of your tuning job.
  • HyperParameterTuningJobConfig: Here, you can configure your tuning options. For example, which parameters you want to tune, the range of values for them, the type of optimization (such as random search or Bayesian search), the maximum number of training jobs you want to spin up, and more.
  • TrainingJobDefinition: Here, you can configure your training job. For example, the data channels, the output location, the resource configurations, the evaluation metrics, and the stop conditions.

In SageMaker...

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AWS Certified Machine Learning - Specialty (MLS-C01) Certification Guide
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