How deeply does Deep Learning with R, Third Edition cover machine learning workflow?

Provides a procedural blueprint covering data, validation, baselines, capacity, regularization, deployment, and monitoring without detailed algorithmic derivations.

Machine learning workflow

As explained in Deep Learning with R, Third Edition

A 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.

A machine learning workflow is a repeatable sequence for turning a real-world problem into a maintained machine learning system. It covers defining the task, developing a model, and deploying it. section 6.0

Use the workflow in this order:

Define the task: understand the domain and business logic, identify available data, decide what to predict, collect or label more data when necessary, and choose a reliable success metric. section 6.0 section 20.1.5
Develop the model: prepare and vectorize the data, establish training, validation, and test sets without label leakage, choose a validation protocol, and beat a trivial baseline. section 6.0 section 20.1.5
Refine and deploy: make the model overfit to identify sufficient capacity, then tune hyperparameters and add regularization using validation results; present, ship, monitor, and collect data for the next model generation. section 6.0 section 20.1.5

Satellite images and archaeological sites

To predict previously unknown archaeological sites from satellite images, first define the prediction target and success metric, then collect and prepare labeled imagery, establish leakage-safe validation, train a baseline and improving model, and finally deploy and monitor it. section 6.0 section 20.1.5

How Deep Learning with R, Third Edition covers it

Cover of Deep Learning with R, Third Edition

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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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Machine Learning Platform EngineeringThe end-to-end sequence of activities through which a machine learning solution progresses from problem formulation and experimentation through deployment, operation, and retirement.
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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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Graph Algorithms for Data ScienceA machine learning workflow is a sequence of processing and modeling steps for training or applying machine learning models.
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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 Deep Learning with R, Third Edition. The section links point to the parts of the book it draws on.