MANDALA.ML: A Life Cycle-Centric and Role-Aware Methodology for Agile Machine Learning Projects
Bibliographic record
Abstract
The successful execution of machine learning (ML) projects requires a mix of professional knowledge, technical and social skills as well as an iterative approach. The range of methodologies previously proposed is extensive, yet there is no “standard”. In recent years, the role of the team in projects as well as questions of governance and ethics have become factors that cannot be ignored, but which are not addressed by major past contenders (such as CRISP-DM, KDD, SEMMA). We therefore propose a novel Methodology for an Adaptive, Navigated, Data-driven and Assessed Lifecycle Approach in Machine Learning, abbreviated as MANDALA.ML, which takes a holistic/integral approach to managing the ML life cycle, ML technology, team, stakeholders, knowledge, IT infrastructure and tools, as well as ethics and governance in a – somewhat Mandala-shaped, hence the name – cycle of cycles. While methodology evaluation is hard and thus not often attempted, we demonstrate analytical and empirical evidence consistent with the thesis that it outperforms past methodologies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".