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FORMATION IA

Fiches Modèles et Documentation IA pour les Équipes ML

Repartez avec un processus de documentation reproductible qui garantit l'auditabilité, la conformité et la fiabilité de vos modèles.

Format
programme
Durée
12–20h
Niveau
practitioner
Taille de groupe
6–18
Prix / participant
€1K–€3K
Prix groupe
€8K–€18K
Public
ML engineers, data scientists, and compliance or risk partners involved in model deployment and governance
Prérequis
Participants should have hands-on experience training or deploying at least one ML model and a basic understanding of data privacy concepts

Ce qu'elle couvre

Ce programme de niveau praticien apprend aux ingénieurs ML, data scientists et partenaires conformité à rédiger des fiches modèles, fiches données et datasheets de haute qualité, conformes aux standards de gouvernance interne et aux exigences réglementaires émergentes. Les participants travaillent sur des modèles de documentation réels, analysent des exemples publiés par les grands laboratoires d'IA et se soumettent à des sessions de revue par les pairs. À l'issue de la formation, les équipes disposent d'un guide de documentation opérationnel et d'au moins une fiche modèle prête pour la production.

À l'issue, vous saurez

  • Write a complete, production-ready model card for an existing model using the Google or Hugging Face template, including performance breakdowns by demographic subgroup
  • Produce a dataset datasheet that documents provenance, collection method, known biases, and intended use restrictions
  • Map documentation requirements to specific EU AI Act obligations for high-risk AI systems
  • Establish a version-controlled documentation workflow integrated with your team's MLOps pipeline
  • Conduct a structured peer review of a colleague's model card and provide actionable, standards-based feedback

Sujets abordés

  • Model card anatomy: intended use, performance metrics, limitations, and ethical considerations
  • Data cards and dataset datasheets: provenance, collection methodology, and known biases
  • Aligning documentation to EU AI Act, GDPR Article 22, and internal risk tiers
  • Writing for multiple audiences: technical peers vs. compliance auditors vs. business stakeholders
  • Version control and lifecycle management for model documentation
  • Peer-review frameworks and documentation quality checklists
  • Integrating model cards into CI/CD and MLOps pipelines
  • Case study analysis of published model cards from Google, Hugging Face, and IBM

Modalité

Delivered as a blended programme over two to three weeks: one live kickoff workshop (half-day, remote or on-site), two live working sessions for draft review and peer critique (two hours each), and self-paced reading and drafting tasks in between. Participants receive editable documentation templates, a curated library of published model cards, and access to a shared review workspace (Notion or Confluence). Hands-on drafting accounts for roughly 60% of total learning time.

Ce qui fait que ça marche

  • Assigning a named documentation owner for each model who is accountable for keeping the card current across the model's lifecycle
  • Integrating model card generation as a required gate in the model release pipeline, not an optional step
  • Building a shared internal library of approved model card examples so teams have realistic benchmarks rather than abstract templates
  • Running quarterly documentation audits where compliance and ML teams jointly review a sample of live model cards

Erreurs fréquentes

  • Writing a single generic model card and treating it as permanent, rather than updating it each time the model is retrained or its scope changes
  • Focusing only on technical metrics while omitting limitations, out-of-scope uses, and fairness considerations that regulators and auditors actually scrutinise
  • Treating documentation as a post-deployment checkbox rather than embedding it in the development workflow from the start
  • Producing model cards readable only by data scientists, with no plain-language section accessible to legal, compliance, or business reviewers

Quand NE PAS suivre cette formation

If a team has no models in production and is still in early exploratory research, investing in formal model card documentation discipline is premature — lightweight internal notes suffice until models approach deployment.

Fournisseurs à considérer

Sources

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