Practical MLOps: Operationalizing Machine Learning Models
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Practical MLOps: Operationalizing Machine Learning Models
Practical MLOps: Operationalizing Machine Learning Models
แƒ”แƒ แƒ—แƒ”แƒฃแƒšแƒ˜แƒก #: 39708191

Practical MLOps: Operationalizing Machine Learning Models

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แƒ”แƒ แƒ—แƒ”แƒฃแƒšแƒ˜แƒก #: 39708191

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Find the best deals on Practical MLOps 1st Edition at Ubuy Georgia. Operationalize your Machine Learning models efficiently with our expert guide!
  • Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models. Current and aspiring machine learning engineers--or anyone familiar with data science and Python--will build a foundation in MLOps tools and methods (along with AutoML and monitoring and logging), then learn how to implement them in AWS, Microsoft Azure, and Google Cloud. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you're trying to crack. This book gives you a head start. You'll discover how to: Apply DevOps best practices to machine learning Build production machine learning systems and maintain them Monitor, instrument, load-test, and operationalize machine learning systems Choose the correct MLOps tools for a given machine learning task Run machine learning models on a variety of platforms and devices, including mobile phones and specialized hardware
Publisher O'Reilly Media
Publication date October 19, 2021
Edition 1st
Language English
Print length 458 pages
ISBN-10 1098103017
ISBN-13 978-1098103019
Item Weight 2.31 pounds (1.05 kg)
Dimensions 6.75 x 1 x 8.75 inches (17.1 x 2.5 x 22.2 cm)

แƒžแƒ แƒแƒ“แƒฃแƒฅแƒขแƒ˜แƒก แƒแƒฆแƒฌแƒ”แƒ แƒ˜แƒšแƒแƒ‘แƒ

แƒ’แƒแƒฅแƒ•แƒ— แƒ แƒแƒ˜แƒ›แƒ” แƒจแƒ”แƒ™แƒ˜แƒ—แƒฎแƒ•แƒ? แƒ›แƒแƒ’แƒ•แƒ›แƒแƒ แƒ—แƒ”แƒ— แƒฉแƒแƒ—แƒจแƒ˜

แƒ›แƒแƒ›แƒฎแƒ›แƒแƒ แƒ”แƒ‘แƒ”แƒšแƒ—แƒ แƒ™แƒ˜แƒ—แƒฎแƒ•แƒ”แƒ‘แƒ˜ แƒ“แƒ แƒžแƒแƒกแƒฃแƒฎแƒ”แƒ‘แƒ˜

  • แƒ™แƒ˜แƒ—แƒฎแƒ•แƒ: แƒ แƒแƒ’แƒแƒ  แƒ•แƒ˜แƒงแƒ˜แƒ“แƒแƒ— Practical MLOps: Operationalizing Machine Learning แƒแƒœแƒšแƒแƒ˜แƒœ Ubuy-แƒ“แƒแƒœ?

    แƒžแƒแƒกแƒฃแƒฎแƒ˜: แƒ›แƒแƒ แƒขแƒ˜แƒ•แƒ˜แƒ แƒงแƒ˜แƒ“แƒ•แƒ Practical MLOps: Operationalizing Machine Learning แƒแƒœแƒšแƒแƒ˜แƒœ Ubuy-แƒ“แƒแƒœ.. แƒ—แƒฅแƒ•แƒ”แƒœ แƒฃแƒ‘แƒ แƒแƒšแƒแƒ“ แƒฃแƒœแƒ“แƒ แƒ›แƒแƒซแƒ”แƒ‘แƒœแƒแƒ— แƒžแƒ แƒแƒ“แƒฃแƒฅแƒขแƒ˜, แƒแƒ˜แƒ แƒฉแƒ˜แƒแƒ— แƒ—แƒฅแƒ•แƒ”แƒœแƒ˜ แƒ›แƒ˜แƒฌแƒแƒ“แƒ”แƒ‘แƒ˜แƒก แƒ›แƒ”แƒ—แƒแƒ“แƒ˜ แƒจแƒ”แƒ›แƒแƒฌแƒ›แƒ”แƒ‘แƒ˜แƒก แƒ“แƒ แƒแƒก แƒ“แƒ แƒ›แƒ˜แƒ˜แƒขแƒแƒœแƒแƒ— แƒ˜แƒ’แƒ˜ แƒ—แƒฅแƒ•แƒ”แƒœแƒก แƒแƒ“แƒ’แƒ˜แƒšแƒแƒก.
  • แƒ™แƒ˜แƒ—แƒฎแƒ•แƒ: Practical MLOps: Operationalizing Machine Learning แƒฎแƒ”แƒšแƒ›แƒ˜แƒกแƒแƒฌแƒ•แƒ“แƒแƒ›แƒ˜แƒ แƒแƒœแƒšแƒแƒ˜แƒœ แƒจแƒแƒžแƒ˜แƒœแƒ’แƒ˜แƒกแƒ—แƒ•แƒ˜แƒก Georgia-แƒจแƒ˜?

    แƒžแƒแƒกแƒฃแƒฎแƒ˜: แƒ“แƒ˜แƒแƒฎ, Ubuy Georgia-แƒจแƒ˜ แƒ”แƒก แƒžแƒ แƒแƒ“แƒฃแƒฅแƒขแƒ˜ แƒ—แƒฅแƒ•แƒ”แƒœแƒ—แƒ•แƒ˜แƒก แƒฎแƒ”แƒšแƒ›แƒ˜แƒกแƒแƒฌแƒ•แƒ“แƒแƒ›แƒ˜แƒ แƒ’แƒแƒœแƒ˜แƒ•แƒ แƒฃแƒš แƒคแƒแƒกแƒแƒ“.. Practical MLOps: Operationalizing Machine Learning แƒแƒ  แƒแƒ แƒ˜แƒก แƒฎแƒ”แƒšแƒ›แƒ˜แƒกแƒแƒฌแƒ•แƒ“แƒแƒ›แƒ˜ แƒแƒ“แƒ’แƒ˜แƒšแƒแƒ‘แƒ แƒ˜แƒ•แƒแƒ“, แƒ›แƒแƒ’แƒ แƒแƒ› แƒจแƒ”แƒ’แƒ˜แƒซแƒšแƒ˜แƒแƒ— แƒ’แƒ•แƒฏแƒ”แƒ แƒแƒ“แƒ”แƒก แƒฉแƒ•แƒ”แƒœแƒ˜ แƒ”แƒฅแƒกแƒžแƒ แƒ”แƒก แƒ›แƒ˜แƒฌแƒแƒ“แƒ”แƒ‘แƒ˜แƒก แƒกแƒ”แƒ แƒ•แƒ˜แƒกแƒ”แƒ‘แƒ˜.
  • แƒ™แƒ˜แƒ—แƒฎแƒ•แƒ: แƒ แƒแƒ›แƒ“แƒ”แƒœแƒ˜ แƒ“แƒ แƒ แƒกแƒญแƒ˜แƒ แƒ“แƒ”แƒ‘แƒ แƒžแƒ แƒแƒ“แƒฃแƒฅแƒขแƒ˜แƒก แƒ›แƒ˜แƒฆแƒ”แƒ‘แƒแƒก แƒจแƒ”แƒ™แƒ•แƒ”แƒ—แƒ˜แƒก แƒ’แƒแƒคแƒแƒ แƒ›แƒ”แƒ‘แƒ˜แƒก แƒจแƒ”แƒ›แƒ“แƒ”แƒ’?

    แƒžแƒแƒกแƒฃแƒฎแƒ˜: แƒ—แƒฅแƒ•แƒ”แƒœแƒ˜ แƒจแƒ”แƒ™แƒ•แƒ”แƒ—แƒ˜แƒšแƒ˜ แƒžแƒ แƒแƒ“แƒฃแƒฅแƒขแƒ˜แƒก แƒ›แƒ˜แƒฌแƒแƒ“แƒ”แƒ‘แƒ˜แƒก แƒ“แƒ แƒ แƒ’แƒแƒœแƒกแƒฎแƒ•แƒแƒ•แƒ“แƒ”แƒ‘แƒ แƒ—แƒฅแƒ•แƒ”แƒœ แƒ›แƒ˜แƒ”แƒ  แƒจแƒ”แƒ™แƒ•แƒ”แƒ—แƒ˜แƒšแƒ˜ แƒ“แƒ แƒ›แƒ˜แƒฌแƒแƒ“แƒ”แƒ‘แƒ˜แƒก แƒ›แƒ”แƒ—แƒแƒ“แƒ˜แƒก แƒ›แƒ˜แƒฎแƒ”แƒ“แƒ•แƒ˜แƒ—, แƒ แƒแƒ›แƒ”แƒšแƒ˜แƒช แƒแƒ แƒฉแƒ”แƒฃแƒšแƒ˜แƒ.. แƒ›แƒ˜แƒฌแƒแƒ“แƒ”แƒ‘แƒ˜แƒก แƒกแƒแƒ•แƒแƒ แƒแƒฃแƒ“แƒ แƒ“แƒ แƒ แƒ›แƒ˜แƒ—แƒ˜แƒ—แƒ”แƒ‘แƒฃแƒšแƒ˜แƒ แƒจแƒ”แƒ™แƒ•แƒ”แƒ—แƒ˜แƒก แƒžแƒ แƒแƒชแƒ”แƒกแƒจแƒ˜, แƒแƒกแƒ” แƒ แƒแƒ› แƒจแƒแƒžแƒ˜แƒœแƒ’แƒ˜แƒก แƒ“แƒ แƒแƒก แƒ˜แƒงแƒแƒ•แƒ˜แƒ— แƒฃแƒงแƒฃแƒ แƒแƒ“แƒฆแƒ”แƒ‘แƒ.

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  • แƒจแƒ”แƒ–แƒฆแƒฃแƒ“แƒ•แƒ”แƒ‘แƒ˜: แƒ’แƒ—แƒฎแƒแƒ•แƒ—, แƒ’แƒแƒ˜แƒ—แƒ•แƒแƒšแƒ˜แƒกแƒฌแƒ˜แƒœแƒแƒ—, แƒ แƒแƒ› แƒกแƒแƒ–แƒฆแƒ•แƒแƒ แƒ’แƒแƒ แƒ”แƒ— แƒ’แƒแƒ’แƒ–แƒแƒ•แƒœแƒ˜แƒšแƒ˜ แƒžแƒ แƒแƒ“แƒฃแƒฅแƒขแƒ”แƒ‘แƒ˜แƒกแƒ—แƒ•แƒ˜แƒก แƒ›แƒฌแƒแƒ แƒ›แƒแƒ”แƒ‘แƒšแƒ˜แƒก แƒ’แƒแƒ แƒแƒœแƒขแƒ˜แƒ แƒจแƒ”แƒ˜แƒซแƒšแƒ”แƒ‘แƒ แƒแƒ  แƒ˜แƒงแƒแƒก แƒ›แƒแƒฅแƒ›แƒ”แƒ“แƒ˜; แƒ›แƒฌแƒแƒ แƒ›แƒแƒ”แƒ‘แƒšแƒ˜แƒก แƒ›แƒแƒ›แƒกแƒแƒฎแƒฃแƒ แƒ”แƒ‘แƒ˜แƒก แƒžแƒแƒ แƒแƒ›แƒ”แƒขแƒ แƒ”แƒ‘แƒ˜ แƒจแƒ”แƒ˜แƒซแƒšแƒ”แƒ‘แƒ แƒแƒ  แƒ˜แƒงแƒแƒก แƒฎแƒ”แƒšแƒ›แƒ˜แƒกแƒแƒฌแƒ•แƒ“แƒแƒ›แƒ˜; แƒžแƒ แƒแƒ“แƒฃแƒฅแƒขแƒ˜แƒก แƒ˜แƒœแƒกแƒขแƒ แƒฃแƒฅแƒชแƒ˜แƒ”แƒ‘แƒ˜ แƒ“แƒ แƒฃแƒกแƒแƒคแƒ แƒ—แƒฎแƒแƒ”แƒ‘แƒ˜แƒก แƒ’แƒแƒคแƒ แƒ—แƒฎแƒ˜แƒšแƒ”แƒ‘แƒ”แƒ‘แƒ˜ แƒจแƒ”แƒ˜แƒซแƒšแƒ”แƒ‘แƒ แƒแƒ  แƒ˜แƒงแƒแƒก แƒ“แƒแƒœแƒ˜แƒจแƒœแƒฃแƒšแƒ”แƒ‘แƒ˜แƒก แƒฅแƒ•แƒ”แƒงแƒœแƒ˜แƒก แƒ”แƒœแƒแƒ–แƒ”; แƒžแƒ แƒแƒ“แƒฃแƒฅแƒขแƒ”แƒ‘แƒ˜ (แƒ“แƒ แƒ›แƒแƒกแƒ—แƒแƒœ แƒ“แƒแƒ™แƒแƒ•แƒจแƒ˜แƒ แƒ”แƒ‘แƒฃแƒšแƒ˜ แƒ›แƒแƒกแƒแƒšแƒ”แƒ‘แƒ˜) แƒจแƒ”แƒ˜แƒซแƒšแƒ”แƒ‘แƒ แƒแƒ  แƒ˜แƒงแƒแƒก แƒจแƒ”แƒ›แƒฃแƒจแƒแƒ•แƒ”แƒ‘แƒฃแƒšแƒ˜ แƒ“แƒแƒœแƒ˜แƒจแƒœแƒฃแƒšแƒ”แƒ‘แƒ˜แƒก แƒฅแƒ•แƒ”แƒงแƒœแƒ˜แƒก แƒกแƒขแƒแƒœแƒ“แƒแƒ แƒขแƒ”แƒ‘แƒ˜แƒก, แƒกแƒžแƒ”แƒชแƒ˜แƒคแƒ˜แƒ™แƒแƒชแƒ˜แƒ”แƒ‘แƒ˜แƒกแƒ แƒ“แƒ แƒ›แƒแƒ แƒ™แƒ˜แƒ แƒ”แƒ‘แƒ˜แƒก แƒ›แƒแƒ—แƒฎแƒแƒ•แƒœแƒ”แƒ‘แƒ˜แƒก แƒจแƒ”แƒกแƒแƒ‘แƒแƒ›แƒ˜แƒกแƒแƒ“; แƒ“แƒ แƒžแƒ แƒแƒ“แƒฃแƒฅแƒขแƒ”แƒ‘แƒ˜ แƒจแƒ”แƒ˜แƒซแƒšแƒ”แƒ‘แƒ แƒแƒ  แƒจแƒ”แƒ”แƒกแƒแƒ‘แƒแƒ›แƒ”แƒ‘แƒแƒ“แƒ”แƒก แƒ“แƒแƒœแƒ˜แƒจแƒœแƒฃแƒšแƒ”แƒ‘แƒ˜แƒก แƒฅแƒ•แƒ”แƒงแƒœแƒ˜แƒก แƒซแƒแƒ‘แƒ•แƒ˜แƒกแƒ แƒ“แƒ แƒกแƒฎแƒ•แƒ แƒ”แƒšแƒ”แƒฅแƒขแƒ แƒ แƒกแƒขแƒแƒœแƒ“แƒแƒ แƒขแƒ”แƒ‘แƒก (แƒกแƒแƒญแƒ˜แƒ แƒแƒ”แƒ‘แƒ˜แƒก แƒจแƒ”แƒ›แƒ—แƒฎแƒ•แƒ”แƒ•แƒแƒจแƒ˜ แƒกแƒแƒญแƒ˜แƒ แƒแƒ แƒแƒ“แƒแƒžแƒขแƒ”แƒ แƒ˜ แƒแƒœ แƒ’แƒแƒ“แƒแƒ›แƒงแƒ•แƒแƒœแƒ˜). แƒ›แƒ˜แƒ›แƒฆแƒ”แƒ‘แƒ˜ แƒžแƒแƒกแƒฃแƒฎแƒ˜แƒกแƒ›แƒ’แƒ”แƒ‘แƒ”แƒšแƒ˜แƒ แƒฃแƒ–แƒ แƒฃแƒœแƒ•แƒ”แƒšแƒงแƒแƒก, แƒ แƒแƒ› แƒžแƒ แƒแƒ“แƒฃแƒฅแƒขแƒ˜ แƒ™แƒแƒœแƒแƒœแƒ˜แƒ”แƒ แƒแƒ“ แƒจแƒ”แƒ›แƒแƒ˜แƒขแƒแƒœแƒแƒก แƒ“แƒแƒœแƒ˜แƒจแƒœแƒฃแƒšแƒ”แƒ‘แƒ˜แƒก แƒฅแƒ•แƒ”แƒงแƒแƒœแƒแƒจแƒ˜. Ubuy-แƒก แƒแƒœ แƒ›แƒ˜แƒกแƒ˜ แƒแƒคแƒ˜แƒšแƒ˜แƒ แƒ”แƒ‘แƒฃแƒšแƒ˜ แƒžแƒ˜แƒ แƒ”แƒ‘แƒ˜แƒก แƒ›แƒ”แƒจแƒ•แƒ”แƒแƒ‘แƒ˜แƒ— แƒจแƒ”แƒ™แƒ•แƒ”แƒ—แƒ˜แƒกแƒแƒก แƒ›แƒ˜แƒ›แƒฆแƒ”แƒ‘แƒ˜ แƒแƒ แƒ˜แƒก แƒ แƒ”แƒ’แƒ˜แƒกแƒขแƒ แƒ˜แƒ แƒ”แƒ‘แƒฃแƒšแƒ˜ แƒ˜แƒ›แƒžแƒแƒ แƒขแƒ˜แƒแƒ แƒ˜ แƒ“แƒ แƒ›แƒแƒœ แƒฃแƒœแƒ“แƒ แƒ’แƒแƒ˜แƒ—แƒ•แƒแƒšแƒ˜แƒกแƒฌแƒ˜แƒœแƒแƒก แƒ“แƒแƒœแƒ˜แƒจแƒœแƒฃแƒšแƒ”แƒ‘แƒ˜แƒก แƒฅแƒ•แƒ”แƒงแƒœแƒ˜แƒก แƒงแƒ•แƒ”แƒšแƒ แƒ™แƒแƒœแƒแƒœแƒ˜ แƒ“แƒ แƒ“แƒ”แƒ‘แƒฃแƒšแƒ”แƒ‘แƒ.
  • Ubuy-แƒ–แƒ” แƒฉแƒแƒ›แƒแƒ—แƒ•แƒšแƒ˜แƒšแƒ˜ แƒงแƒ•แƒ”แƒšแƒ แƒžแƒ แƒแƒ“แƒฃแƒฅแƒขแƒ˜ แƒแƒ  แƒแƒ แƒ˜แƒก แƒ’แƒแƒงแƒ˜แƒ“แƒ•แƒแƒจแƒ˜, แƒ แƒแƒ“แƒ’แƒแƒœ Ubuy แƒแƒ แƒ˜แƒก แƒ’แƒšแƒแƒ‘แƒแƒšแƒฃแƒ แƒ˜ แƒกแƒแƒซแƒ˜แƒ”แƒ‘แƒ แƒกแƒ˜แƒกแƒขแƒ”แƒ›แƒ แƒ“แƒ แƒžแƒ แƒแƒ“แƒฃแƒฅแƒขแƒ”แƒ‘แƒ˜ แƒ”แƒฅแƒ•แƒ”แƒ›แƒ“แƒ”แƒ‘แƒแƒ แƒ”แƒ‘แƒ แƒกแƒแƒ”แƒฅแƒกแƒžแƒแƒ แƒขแƒ/แƒกแƒแƒ•แƒแƒญแƒ แƒ แƒ แƒ”แƒ’แƒฃแƒšแƒแƒชแƒ˜แƒ”แƒ‘แƒก.

GEL 226

GEL 340

แƒจแƒ”แƒฃแƒ™แƒ•แƒ”แƒ—แƒ”แƒ— แƒแƒฎแƒšแƒแƒ•แƒ” แƒ“แƒ แƒ›แƒ˜แƒ˜แƒฆแƒ”แƒ— แƒžแƒแƒ แƒแƒกแƒ™แƒ”แƒ•แƒ˜, แƒแƒฅแƒขแƒแƒ›แƒ‘แƒ”แƒ แƒ˜ 09

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