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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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SageMaker Feature Store

Imagine you are building a recommendation system. In the absence of Feature Store, you’d navigate a landscape of manual feature engineering, scattered feature storage, and constant vigilance for consistency.

Feature management in an ML pipeline is challenging due to the dispersed nature of feature engineering, involving various teams and tools. Collaboration issues arise when different teams handle different aspects of feature storage, leading to inconsistencies and versioning problems. The dynamic nature of features evolving over time complicates change tracking and ensuring reproducibility. SageMaker Feature Store addresses these challenges by providing a centralized repository for features, enabling seamless sharing, versioning, and consistent access across the ML pipeline, thus simplifying collaboration, enhancing reproducibility, and promoting data consistency.

Now, user data, including age, location, browsing history, and item data such as...

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