How deeply does Distributed Machine Learning Patterns cover Kubeflow?

Defines core terminology and provides hands-on job submission, while deferring complete implementation details to an end-to-end project.

Kubeflow

As explained in Distributed Machine Learning Patterns

An open-source platform for deploying and orchestrating portable, scalable machine learning workflows on Kubernetes.

Kubeflow is a platform for submitting distributed machine-learning training jobs to Kubernetes clusters. It sits above Kubernetes, while TensorFlow defines data processing, model building, and evaluation. section 8.0 section 1.4

Use Kubeflow to:

Run distributed model-training logic in a Kubernetes cluster. section 9.5
Connect model training with an end-to-end workflow orchestrated by Argo Workflows. section 8.0
Move from single-machine experiments toward large distributed clusters. section 1.4

Distributed model training

Train a model with TensorFlow, then execute its distributed training logic in a Kubernetes cluster through Kubeflow. In the complete pipeline, TensorFlow also provides the distributed input pipeline for Fashion-MNIST. section 9.5 section 8.10

Table 1.1 The technologies covered in this book and their uses (view table figure)

Technology	Use
TensorFlow	Building machine learning and deep learning models
Kubernetes	Managing distributed environments and resources
Kubeflow	Submitting and managing distributed training jobs easily on Kubernetes clusters
Argo Workflows	Defining, orchestrating, and managing workflows
Docker	Building and managing images to be used for starting containerized environments

Compare Kubeflow with the neighboring technologies: Kubernetes manages distributed resources, TensorFlow builds models, Argo Workflows orchestrates workflows, and Docker supplies container images. section 1.4

How Distributed Machine Learning Patterns covers it

Cover of Distributed Machine Learning Patterns

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Machine Learning Platform EngineeringKubeflow Pipelines is a Kubeflow component and software development kit for defining, executing, and orchestrating machine-learning pipelines.

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Related concepts

ML Metadatauses · An open-source library for recording, storing, and querying metadata about machine learning workflows and their artifacts.
Kubernetesuses · Kubernetes is an open-source platform for orchestrating and automating the deployment, scaling, and management of containerized workloads across computing clusters.
Kubeflow training operatorhas part · The Kubeflow Training Operator is a Kubernetes operator that manages distributed and nondistributed machine learning training jobs through custom resources.
machine learning infrastructurekind of · Machine learning infrastructure is the hardware, software, data, and services supporting the development, training, deployment, and operation of machine-learning systems.
pipeline componentuses · A pipeline component is a discrete, executable stage in a data or machine-learning workflow that performs an operation and may consume or produce data artifacts.
directed acyclic graphuses · A directed acyclic graph is a graph with directed edges and no directed cycles.

This explainer was generated by Manning's knowledge graph from the text of Distributed Machine Learning Patterns. The section links point to the parts of the book it draws on.