Developing a maturity-based workflow for the implementation of ML-applications using the example of a demand forecast
Bibliographic record
Abstract
The aim of the article is to present a guideline that has been developed in the form of a workflow to identify the capability of an organisation to implement machine learning (ML) applications on the one hand and, on the other hand, to describe a maturity-dependent procedure for the development of an ML application based on this knowledge. With the help of the guideline, application-specific requirements can be identified based on the phases of the development process of an ML application adapted to the corporate environment. The article begins with the motivation for using machine learning methods and presents the challenges in implementing these methods. Based on a literature review, a maturity-based approach is designed and the developed and adapted development phases from the literature are described in a more detailed way. The individual characteristics of certain phases are specified based on the maturity level. As well, the weighting of certain maturity dimensions of the respective phase is highlighted. The article ends with an outlook on the further development of the created guideline.
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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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".