How deeply does Graph Algorithms for Data Science cover machine learning workflow?

Explains preprocessing rationale and feature construction at a high level, deferring detailed graph transformations to later techniques.

Machine learning workflow

As explained in Graph Algorithms for Data Science

A machine learning workflow is a sequence of processing and modeling steps for training or applying machine learning models.

A machine learning workflow sequences data transformation, feature construction, and model training or application to solve a specified task. Feature engineering and data wrangling often represent most of the analytical workload. section 2.0

Graph-based workflows follow the same broad pattern as traditional machine learning, but differ in how model features are extracted. Graph data may need reshaping before algorithms can process it. section 2.0

A practical workflow includes:

Transform a graph with multiple node or relationship types into the structure expected by an algorithm. section 2.0
Construct features from the transformed graph before executing an algorithm or training a model. section 2.0
Use graph machine learning for prediction or classification, rather than descriptive analysis such as community detection or centrality. section 2.0

How Graph Algorithms for Data Science covers it

Cover of Graph Algorithms for Data Science

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Also explained in

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Distributed Machine Learning PatternsThe stages and activities involved in developing, deploying, operating, maintaining, and retiring machine learning models and systems.
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Deep Learning with Python, Third EditionThe end-to-end sequence of steps for defining, building, evaluating, deploying, and maintaining a machine learning system.
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Deep Learning with R, Third EditionA machine learning workflow is an organized sequence of activities for defining a problem, preparing data, developing a model, and deploying and maintaining the resulting system.
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The Art of AI Product DevelopmentA machine learning lifecycle is an iterative process for preparing data, developing models, evaluating results, and applying machine-learning outputs.
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LLMs in ProductionA machine learning life cycle is the sequence of activities involved in creating, deploying, operating, and retiring machine-learning models.

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Related concepts

pipelineequivalent to · A pipeline is an ordered or coordinated sequence of connected processing stages or tasks that passes data, work, or results toward an outcome.
traininghas part · Model training is the process of fitting a machine-learning model to data by adjusting its parameters or structure to perform a task.
monitoringhas part · Monitoring is the ongoing observation and measurement of a system’s behavior, state, or operational signals to assess its condition and detect changes or problems.
preprocessinghas part · Preprocessing is the preparation or transformation of data, content, or other inputs into a form suitable for a subsequent computational operation.
Model Servinghas part · A model-serving platform is software and infrastructure that hosts trained machine-learning models, handles inference requests, and returns model outputs.
generationuses · Generation is the computational production of outputs from supplied inputs or context, often by applying a model to produce predictions or new content.

This explainer was generated by Manning's knowledge graph from the text of Graph Algorithms for Data Science. The section links point to the parts of the book it draws on.