Deploying automated ticket router across the enterprise
Bibliographic record
Abstract
Abstract With the recent advances in machine learning, the use of natural language processing (NLP) technology to support various business processes has been increasing. This paper discusses the use of NLP to route more than one million live client tickets annually to the appropriate service personnel in 67 support missions across IBM. Each mission supports a product family with multiple support teams, each requiring different skills for the engineers. We discuss three important aspects of such a large‐scale deployment: (i) The use of a centralized team with a common machine learning infrastructure and practices to support the entire enterprise. (ii) The processes and quality of such a deployment from the perspective of one support mission, namely, IBM's z/OS family. (iii) Careful monitoring of the deployed models to detect drifts in the routing behavior. Despite vast differences in the technical contents of the support missions, it is possible to define common processes and metrics across the enterprise, without requiring a dedicated machine learning team for each mission. In addition, we provide examples of the business policies and metrics from the perspective of the z/OS mission to demonstrate the utility of the approach and the outcome.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".