Distributed Machine Learning Patterns / Nejlevnější knihy
Distributed Machine Learning Patterns

Kód: 37301710

Distributed Machine Learning Patterns

Autor Tang, Yuan

Practical patterns for scaling machine learning from your laptop to a distributed cluster. In  Distributed Machine Learning Patterns you will learn how to: Apply distributed systems patterns to build scalable and reliable ma ... celý popis

1492 Kč

Dostupnost:

50 % šanceMáme informaci, že by titul mohl být dostupný. Na základě vaší objednávky se ho pokusíme do 6 týdnů zajistit.
Prohledáme celý svět

Informovat o naskladnění

Přidat mezi přání

Mohlo by se vám také líbit

Dárkový poukaz: Radost zaručena

Objednat dárkový poukazVíce informací

Informovat o naskladnění knihy

Informovat o naskladnění knihy


Souhlas - Souhlasím se zasíláním obchodních sdělení a zpracováním osobních údajů k obchodním sdělením.

Zašleme vám zprávu jakmile knihu naskladníme

Zadejte do formuláře e-mailovou adresu a jakmile knihu naskladníme, zašleme vám o tom zprávu. Pohlídáme vše za vás.

Více informací o knize Distributed Machine Learning Patterns

Nákupem získáte 149 bodů

Anotace knihy

Practical patterns for scaling machine learning from your laptop to a distributed cluster.

In  Distributed Machine Learning Patterns you will learn how to:

  • Apply distributed systems patterns to build scalable and reliable machine learning projects
  • Construct machine learning pipelines with data ingestion, distributed training, model serving, and more
  • Automate machine learning tasks with Kubernetes, TensorFlow, Kubeflow, and Argo Workflows
  • Make trade offs between different patterns and approaches
  • Manage and monitor machine learning workloads at scale
Scaling up models from standalone devices to large distributed clusters is one of the biggest challenges faced by modern machine learning practitioners. Distributed Machine Learning Patterns teaches you how to scale machine learning models from your laptop to large distributed clusters. 

In Distributed Machine Learning Patterns, you''ll learn how to apply established distributed systems patterns to machine learning projects, and explore new ML-specific patterns as well. Firmly rooted in the real world, this book demonstrates how to apply patterns using examples based in TensorFlow, Kubernetes, Kubeflow, and Argo Workflows. Real-world scenarios, hands-on projects, and clear, practical DevOps techniques let you easily launch, manage, and monitor cloud-native distributed machine learning pipelines

Distributed Machine Learning Patterns teaches you how to scale machine learning models from your laptop to large distributed clusters. In it, you''ll learn how to apply established distributed systems patterns to machine learning projects, and explore new ML-specific patterns as well. Firmly rooted in the real world, this book demonstrates how to apply patterns using examples based in TensorFlow, Kubernetes, Kubeflow, and Argo Workflows. Real-world scenarios, hands-on projects, and clear, practical DevOps techniques let you easily launch, manage, and monitor cloud-native distributed machine learning pipelines.

about the technology

Scaling up models from standalone devices to large distributed clusters is one of the biggest challenges faced by modern machine learning practitioners. Distributing machine learning systems allow developers to handle extremely large datasets across multiple clusters, take advantage of automation tools, and benefit from hardware accelerations. In this book, Kubeflow co-chair Yuan Tang shares patterns, techniques, and experience gained from years spent building and managing cutting-edge distributed machine learning infrastructure.

about the book

Distributed Machine Learning Patterns is filled with practical patterns for running machine learning systems on distributed Kubernetes clusters in the cloud. Each pattern is designed to help solve common challenges faced when building distributed machine learning systems, including supporting distributed model training, handling unexpected failures, and dynamic model serving traffic. Real-world scenarios provide clear examples of how to apply each pattern, alongside the potential trade offs for each approach. Once you''ve mastered these cutting edge techniques, you''ll put them all into practice and finish up by building a comprehensive distributed machine learning system.

Parametry knihy

Zařazení knihy Knihy v němčině Naturwissenschaften, Medizin, Informatik, Technik Informatik, EDV Informatik

1492 Kč

Oblíbené z jiného soudku

O tomto obchodě



Nákupní rádce
Přehledy
Knihy podle jazyka
Platba
Doručení 54 Kč

Osobní odběr Praha, Brno a 46876 dalších

Česko
България Hrvatska România Magyarország Polska Slovensko

Copyright ©2008-26 nejlevnejsi-knihy.cz Všechna práva vyhrazenaSoukromíCookies

Můj účet: Přihlásit se
Všechny knihy světa na jednom místě. Navíc za skvělé ceny.

Nákupní košík ( prázdný )

Vyzvednutí v Balikovně a PPL
boxech zdarma nad 1 499 Kč.

Nacházíte se: