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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 : Nanda, 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: Nanda, 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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SageMaker Debugger

In this section, you will learn about Amazon SageMaker Debugger, unraveling the intricacies of monitoring, profiling, and debugging ML model training:

  • Monitoring and profiling: SageMaker Debugger captures model metrics and keeps a real-time eye on system resources during training, eliminating the need for additional code. It not only provides a window into the training process but empowers instant issue correction, expediting training and elevating model quality.
  • Automatic detection and analysis: A true time-saver, Debugger automatically spots and notifies you of common training errors, such as oversized or undersized gradient values. Say goodbye to days of troubleshooting; Debugger reduces it to mere hours.
  • Profiling capabilities: Venture into the realm of profiling with Debugger, which meticulously monitors system resource utilization metrics and allows you to profile training jobs. This involves collecting detailed metrics from your ML framework...
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AWS Certified Machine Learning - Specialty (MLS-C01) Certification Guide
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