Architectural Design Decisions for Machine Learning Workflows

A qualitative research artifact from the University of Vienna contains source codings, an audit trail, and a generated Architectural Design Decision model for machine learning workflows. The dataset supports a Straussian Grounded Theory study of practitioner views documented in gray literature. It includes Python applications for generating results and a detailed description of the research method.

Use Cases

• Analyzing architectural decision patterns based on the source codings and audit trail
• Generating publication-ready tables and models using the provided Python applications
• Studying practitioner practices in data processing and model building as documented in the research
• Extending the Architectural Design Decision model for specific ML workflow contexts

Strengths

• Includes a complete audit trail and source codings, supporting research reproducibility
• Provides Python generators to reproduce the model and publication materials
• Artifact is associated with a peer-reviewed article published on paperswithcode

Limitations

• Row count and dataset size are unknown, which may limit suitability assessment
• Column-level documentation is absent; field semantics must be inferred from the research article
• Last update date is unknown; freshness unverified

Provenance

Source

University of Vienna

Collection Method

Straussian Grounded Theory applied to gray literature sources