Defined scope
- Dataset preparation
- Validation gates
- Model training inputs
- Evaluation reports
- Governance checkpoints
CONCEPT
Machine Learning Workflows
Machine learning workflows are the ordered practices that move from source material and metadata through validation, preparation, training, evaluation and deployment decisions.
Machine learning workflows depend on source quality, dataset structure, documentation, evaluation and governance as much as on model architecture.
active
validated
v1.0.0
EDITORIAL FRAME
What this entry establishes.
A concise view of its scope, position, limitations and supporting sources.
Editorial position
- Workflow quality begins before training, at the point where source material is captured, reviewed and documented.
- Clear boundaries between preparation, training and deployment reduce misleading AI claims.
Explicit limits
- Isolated model prompts with no data lifecycle
- Claims about training quality without source evidence
Tags and disciplines
Machine LearningWorkflowDataset PreparationEvaluationArtificial IntelligenceData EngineeringSoftware Architecture
Definition
Machine learning workflows are the practical sequences that connect source material, metadata, validation, model work and evaluation.
Scope
The concept includes dataset capture, quality gates, manifests, transformation, training inputs, evaluation reports and governance checkpoints.
Applications
Electronic Artefacts uses the concept to separate dataset preparation from later model training, especially for sensitive media such as voice.
Limits
Preparing a dataset is not the same as training a model. A responsible workflow keeps those stages visible and separately governed.
Machine Learning Workflows
Cite this page
Machine Learning Workflows. 1.0.0. Electronic Artefacts, 2026-07-09. https://electronicartefacts.com/knowledge/concepts/machine-learning-workflows/
TYPED RELATIONSHIPS
Connected work and ideas.
Each relation names what connects the two entries and why that connection matters.
Voice Capture Studio
Voice Capture Studio supports downstream machine-learning workflows by preparing accepted recordings and metadata without performing model training itself.
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