Machine Learning with Amazon SageMaker Cookbook. 80 proven recipes for data scientists and developers to perform machine learning experiments and deployments

(ebook) (audiobook) (audiobook)

Autor:: Joshua Arvin Lat

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Wydawnictwo:: Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
Ocena:: Bądź pierwszym, który oceni tę książkę
Stron:: 762
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Do przechowalni

Amazon SageMaker is a fully managed machine learning (ML) service that helps data scientists and ML practitioners manage ML experiments. In this book, you'll use the different capabilities and features of Amazon SageMaker to solve relevant data science and ML problems.
This step-by-step guide features 80 proven recipes designed to give you the hands-on machine learning experience needed to contribute to real-world experiments and projects. You'll cover the algorithms and techniques that are commonly used when training and deploying NLP, time series forecasting, and computer vision models to solve ML problems. You'll explore various solutions for working with deep learning libraries and frameworks such as TensorFlow, PyTorch, and Hugging Face Transformers in Amazon SageMaker. You'll also learn how to use SageMaker Clarify, SageMaker Model Monitor, SageMaker Debugger, and SageMaker Experiments to debug, manage, and monitor multiple ML experiments and deployments. Moreover, you'll have a better understanding of how SageMaker Feature Store, Autopilot, and Pipelines can meet the specific needs of data science teams.
By the end of this book, you'll be able to combine the different solutions you've learned as building blocks to solve real-world ML problems.

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O autorze książki

Joshua Arvin Lat is the Chief Technology Officer (CTO) of NuWorks Interactive Labs, Inc. He previously served as the CTO of three Australian-owned companies and also served as the Director for Software Development and Engineering for multiple e-commerce start-ups in the past, which allowed him to be more effective as a leader. Years ago, he and his team won first place in a global cybersecurity competition with their published research paper. He is also an AWS Machine Learning Hero and has shared his knowledge at several international conferences, discussing practical strategies on machine learning, engineering, security, and management.

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Oceny i opinie klientów: Machine Learning with Amazon SageMaker Cookbook. 80 proven recipes for data scientists and developers to perform machine learning experiments and deployments Joshua Arvin Lat (0) Weryfikacja opinii następuję na podstawie historii zamówień na koncie Użytkownika umieszczającego opinię. Użytkownik mógł otrzymać punkty za opublikowanie opinii uprawniające do uzyskania rabatu w ramach Programu Punktowego.

Szczegóły książki

Tytuł oryginału:: Machine Learning with Amazon SageMaker Cookbook. 80 proven recipes for data scientists and developers to perform machine learning experiments and deployments
ISBN Ebooka:: 978-18-005-6612-5, 9781800566125
Data wydania ebooka :: 2021-10-29 Data wydania ebooka często jest dniem wprowadzenia tytułu do sprzedaży i może nie być równoznaczna z datą wydania książki papierowej. Dodatkowe informacje możesz znaleźć w darmowym fragmencie. Jeśli masz wątpliwości skontaktuj się z nami sklep@helion.pl.
Język publikacji:: 1
Rozmiar pliku Pdf:: 29.5MB
Rozmiar pliku ePub:: 31.7MB
Rozmiar pliku Mobi:: 31.7MB

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Kategorie:
Programowanie » Python - Programowanie
Programowanie » Programowanie w chmurze
DevOps » Amazon Web Services
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Spis treści książki

1. Getting Started with Machine Learning Using Amazon SageMaker
2. Building and Using your own Algorithm Container Image
3. Using Machine Learning and Deep Learning Frameworks with Amazon SageMaker
4. Preparing, Processing, and Analyzing the Data
5. Effectively Managing Machine Learning Experiments
6. Automated Machine Learning in Amazon SageMaker
7. Working with SageMaker Feature Store, SageMaker Clarify, and SageMaker Model Monitor
8. Solving NLP, Image Classification, and Time-Series Forecasting Problems with Built-in Algorithms
9. Managing Machine Learning Workflows and Deployments

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