Distributed Machine Learning Patterns (MEAP V07)

✍ Scribed by Yuan Tang

Publisher: Manning Publications
Year: 2023
Tongue: English
Leaves: 295
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

Practical patterns for scaling machine learning from your laptop to a distributed cluster. 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. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

✦ Table of Contents

Copyright_2023_Manning_Publications
welcome
1_Introduction_to_distributed_machine_learning_systems
2_Data_ingestion_patterns
3_Distributed_training_patterns
4_Model_serving_patterns
5_Workflow_patterns
6_Operation_patterns
7_Project_overview_and_system_architecture
8_Overview_of_relevant_technologies
9_A_complete_implementation

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