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Beschreibung
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.
Distributing machine learning systems allow developers to handle extremely large datasets across multiple clusters, take advantage of automation tools, and benefit from hardware accelerations.
Distributing machine learning systems allow developers to handle extremely large datasets across multiple clusters, take advantage of automation tools, and benefit from hardware accelerations.
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.
Distributing machine learning systems allow developers to handle extremely large datasets across multiple clusters, take advantage of automation tools, and benefit from hardware accelerations.
Distributing machine learning systems allow developers to handle extremely large datasets across multiple clusters, take advantage of automation tools, and benefit from hardware accelerations.
Über den Autor
Yuan Tang is currently a founding engineer at Akuity. Previously he was a senior software engineer at Alibaba Group, building AI infrastructure and AutoML platforms on Kubernetes. Yuan is co-chair of Kubeflow, maintainer of Argo, TensorFlow, XGBoost, and Apache MXNet. He is the co-author of TensorFlow in Practice and author of the TensorFlow implementation of Dive into Deep Learning.
Inhaltsverzeichnis
table of contents
PART 1: BASIC CONCEPTS AND BACKGROUND
READ IN LIVEBOOK1INTRODUCTION TO DISTRIBUTED MACHINE LEARNING SYSTEMS
PART 2: PATTERNS OF DISTRIBUTED MACHINE LEARNING SYSTEMSREAD IN LIVEBOOK2DATA INGESTION PATTERNS
READ IN LIVEBOOK3DISTRIBUTED TRAINING PATTERNS
READ IN LIVEBOOK4MODEL SERVING PATTERNS
READ IN LIVEBOOK5WORKFLOW PATTERNS
READ IN LIVEBOOK6OPERATION PATTERNS
PART 3: BUILDING A DISTRIBUTED MACHINE LEARNING PIPELINE7 OVERVIEW OF PROJECT ARCHITECTURE
8 OVERVIEW OF RELEVANT TECHNOLOGIES
9 A COMPLETE IMPLEMENTATION
Details
Erscheinungsjahr: | 2024 |
---|---|
Genre: | Informatik |
Rubrik: | Naturwissenschaften & Technik |
Medium: | Taschenbuch |
Inhalt: | Kartoniert / Broschiert |
ISBN-13: | 9781617299025 |
ISBN-10: | 1617299022 |
Sprache: | Englisch |
Einband: | Kartoniert / Broschiert |
Autor: | Tang, Yuan |
Hersteller: | Manning Publications |
Maße: | 187 x 235 x 17 mm |
Von/Mit: | Yuan Tang |
Erscheinungsdatum: | 17.01.2024 |
Gewicht: | 0,498 kg |
Über den Autor
Yuan Tang is currently a founding engineer at Akuity. Previously he was a senior software engineer at Alibaba Group, building AI infrastructure and AutoML platforms on Kubernetes. Yuan is co-chair of Kubeflow, maintainer of Argo, TensorFlow, XGBoost, and Apache MXNet. He is the co-author of TensorFlow in Practice and author of the TensorFlow implementation of Dive into Deep Learning.
Inhaltsverzeichnis
table of contents
PART 1: BASIC CONCEPTS AND BACKGROUND
READ IN LIVEBOOK1INTRODUCTION TO DISTRIBUTED MACHINE LEARNING SYSTEMS
PART 2: PATTERNS OF DISTRIBUTED MACHINE LEARNING SYSTEMSREAD IN LIVEBOOK2DATA INGESTION PATTERNS
READ IN LIVEBOOK3DISTRIBUTED TRAINING PATTERNS
READ IN LIVEBOOK4MODEL SERVING PATTERNS
READ IN LIVEBOOK5WORKFLOW PATTERNS
READ IN LIVEBOOK6OPERATION PATTERNS
PART 3: BUILDING A DISTRIBUTED MACHINE LEARNING PIPELINE7 OVERVIEW OF PROJECT ARCHITECTURE
8 OVERVIEW OF RELEVANT TECHNOLOGIES
9 A COMPLETE IMPLEMENTATION
Details
Erscheinungsjahr: | 2024 |
---|---|
Genre: | Informatik |
Rubrik: | Naturwissenschaften & Technik |
Medium: | Taschenbuch |
Inhalt: | Kartoniert / Broschiert |
ISBN-13: | 9781617299025 |
ISBN-10: | 1617299022 |
Sprache: | Englisch |
Einband: | Kartoniert / Broschiert |
Autor: | Tang, Yuan |
Hersteller: | Manning Publications |
Maße: | 187 x 235 x 17 mm |
Von/Mit: | Yuan Tang |
Erscheinungsdatum: | 17.01.2024 |
Gewicht: | 0,498 kg |
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