| Description |
1 online resource (xix, 225 pages) : illustrations. |
| Note |
Includes index. |
| Summary |
Inside Distributed Machine Learning Patterns you'll learn to apply established distributed systems patterns to machine learning projects-plus explore cutting-edge new patterns created specifically for machine learning. Firmly rooted in the real world, this book demonstrates how to apply patterns using examples based in TensorFlow, Kubernetes, Kubeflow, and Argo Workflows. Hands-on projects and clear, practical DevOps techniques let you easily launch, manage, and monitor cloud-native distributed machine learning pipelines. |
| Contents |
Part 1. Basic concepts and background. Introduction to distributed machine learning system -- Part 2. Patterns of distributed machine learning systems. Data ingestion patterns -- Distributed training patterns -- Model serving patterns -- Workflow patterns -- Operation patterns -- Part 3. Building a distributed machine learning workflow. Project overview and system architecture -- A complete implementation. |
| Subject |
Kubernetes.
|
|
Electronic data processing -- Distributed processing.
|
|
Machine learning.
|
|
Traitement réparti. |
|
Apprentissage automatique. |
| Other Form: |
Print version: Tang, Yuan. Distributed machine learning patterns. Shelter Island, NY : Manning Publications, 2023 9781617299025 (OCoLC)1402274083 |
| ISBN |
9781617299025 (electronic bk.) |
|
1617299022 (electronic bk.) |
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