FORMATION IA
Fondamentaux de la Gestion de Projets IA
Donnez aux chefs de projet les outils pour piloter et livrer des initiatives IA sans les erreurs classiques.
Ce qu'elle couvre
Ce programme prépare les chefs de projet expérimentés à gérer les défis spécifiques à la livraison de projets d'intelligence artificielle et de machine learning. Les participants apprennent les cadres de livraison itérative adaptés à l'IA, comment calibrer les attentes des parties prenantes face à l'incertitude des modèles, et comment concevoir des jalons tenant compte des dépendances aux données et de la dérive des modèles. Le format combine des sessions en présentiel ou à distance, des études de cas et des simulations de sprints pratiques sur deux à quatre semaines.
À l'issue, vous saurez
- Design a stage-gated delivery plan for an AI project that includes data readiness checks, model evaluation checkpoints, and a post-deployment review cycle
- Produce a stakeholder communication pack that accurately frames model confidence intervals and expected performance ranges to non-technical audiences
- Identify and mitigate the top five AI-specific project risks — including data drift, labelling delays, and compute overruns — using a structured risk register template
- Apply an iterative AI delivery framework (e.g. ML-Scrum or CRISP-DM) to break an AI initiative into sprint-ready increments with clear acceptance criteria
- Define business-aligned success metrics that connect model performance outputs to measurable operational or revenue outcomes
Sujets abordés
- Iterative and adaptive delivery frameworks for AI (CRISP-DM, ML-Scrum hybrids)
- Stakeholder expectation management under model uncertainty
- Stage gates and go/no-go criteria specific to AI projects
- Data dependency mapping and pipeline risk management
- Model drift, retraining cycles, and post-deployment governance
- AI project risk patterns: data quality, labelling bottlenecks, compute costs
- Team composition and roles in an AI delivery squad
- Metrics and reporting for AI projects: accuracy vs. business KPIs
Modalité
Typically delivered as four live sessions of four to six hours each, spread over two to four weeks, with async pre-reading and case study preparation between sessions. Can be run fully remote via video conferencing or in a hybrid classroom format. Materials include a project planning canvas, a risk register template, a stakeholder briefing deck template, and sprint simulation scenario packs. Hands-on exercises account for approximately 50% of contact time.
Ce qui fait que ça marche
- Involve data engineers and ML engineers in sprint planning from day one so technical constraints surface early
- Establish a living data readiness checklist as a formal entry criterion for each project phase
- Run a lightweight pilot sprint with real data before committing to full delivery scope and timeline
- Create a post-deployment runbook covering drift thresholds, retraining triggers, and escalation paths before go-live
Erreurs fréquentes
- Applying waterfall or fixed-scope contracts to AI projects where requirements and feasibility evolve with every data discovery cycle
- Setting binary success criteria (works / doesn't work) instead of probabilistic performance thresholds tied to business value
- Underestimating data acquisition and labelling effort, leading to blown timelines and budget overruns in the first sprint
- Treating model deployment as the final milestone and ignoring monitoring, retraining, and governance costs in the project plan
Quand NE PAS suivre cette formation
This training is not the right fit for a team that has already shipped multiple AI products and is looking to optimise MLOps pipelines or model governance at scale — they need a more technical, advanced curriculum rather than delivery fundamentals.
Fournisseurs à considérer
Sources
Cette formation fait partie d'un catalogue Data & IA construit pour les leaders sérieux sur l'exécution. Lancez le diagnostic gratuit pour voir quelles formations sont prioritaires pour votre équipe.