{
  "abstract": "STAMM (Soft sensor moniToring and mAintenance framework for Machine learning Models) is an open source MLOps framework for industrial machine-learning soft sensors. Unlike general-purpose MLOps platforms (MLflow, Kubeflow, Metaflow, ClearML), STAMM targets the regime where ground-truth labels arrive of fline hours or days late, processes exhibit slow non-stationary dynamics, and models are multi-language. The framework comprises five loosely coupled components: a time-series database, workflow orchestrator, language agnostic REST model registry, dashboard with human-in-the-loop labelling, and an extensible drift-detection package. STAMM was validated on an industrial-scale fed-batch penicillin fermentation (IndPenSim) with seven coexisting R and Python soft sensors served through the model registry.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "name": "Carlos Suarez"
    },
    {
      "name": "Alexander Astudillo"
    },
    {
      "name": "Brett Metcalfe"
    },
    {
      "name": "Matthew Crowther"
    },
    {
      "name": "Jasper J Koehorst"
    },
    {
      "name": "Esteban Castillo"
    },
    {
      "name": "Ariane Bize"
    },
    {
      "name": "David Camilo Corrales"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/agbhv3tavaeen.bib",
  "cited_by": 1,
  "doi": "10.1016/j.softx.2026.102783",
  "fulltext_url": null,
  "id": "agbhv3tavaeen",
  "issns": [
    "2352-7110"
  ],
  "landing_url": "https://hal.inrae.fr/hal-05781695v1",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "hal",
      "0.sdv"
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    [
      "hal",
      "1.sdv.bio"
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      "openalex",
      "2207|Fault Detection and Control Systems"
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  "openalex": "W7164814591",
  "pdf_url": "https://hal.inrae.fr/hal-05781695/document",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-06",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/agbhv3tavaeen.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:49Z",
      "native_id": "hal-05781695",
      "source": "hal",
      "url": "https://hal.inrae.fr/hal-05781695v1"
    }
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  "subjects": [
    {
      "field": "biology",
      "origin": "hal:1.sdv.bio",
      "subfield": "systems-synthetic"
    }
  ],
  "title": "STAMM: Soft sensor moniToring and mAintenance framework for Machine learning Models",
  "title_text": "STAMM: Soft sensor moniToring and mAintenance framework for Machine learning Models",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:49Z",
  "url": "https://research.draconitic.com/paper/agbhv3tavaeen",
  "venue": "SoftwareX",
  "versions": []
}