FORMATION IA
Ingénierie de l'IA Multimodale pour les Équipes Produit
Construisez des systèmes de production combinant vision, texte et audio avec les LLM multimodaux les plus avancés.
Ce qu'elle couvre
Ce programme de niveau praticien permet aux ingénieurs et aux équipes produit de concevoir, construire et déployer des systèmes d'IA multimodaux intégrant des entrées visuelles, textuelles et audio. Les participants travaillent en pratique avec les principaux modèles multimodaux — GPT-4o, Claude Vision et Gemini — et apprennent à choisir la bonne architecture pour des cas d'usage concrets tels que l'intelligence documentaire, la Q&R visuelle ou les pipelines de transcription audio. Le cours aborde le prompt engineering pour les entrées multimodales, les techniques de grounding, le parsing des sorties et les stratégies d'évaluation spécifiques aux modalités non textuelles. Le format alterne sessions de live coding, labs guidés et ateliers de revue d'architecture.
À l'issue, vous saurez
- Send structured multimodal prompts (image + text) to GPT-4o, Claude Vision, and Gemini and parse their outputs reliably in production code
- Design an end-to-end document intelligence pipeline combining OCR, layout detection, and visual Q&A
- Build an audio processing workflow that transcribes, diarises, and routes spoken input to downstream actions
- Select the appropriate multimodal model and architecture for a given cost, latency, and accuracy constraint
- Evaluate multimodal system quality using task-specific metrics beyond simple accuracy
Sujets abordés
- Architecture of multimodal LLMs: how vision, text and audio encoders are unified
- Hands-on with GPT-4o, Claude Vision, and Gemini multimodal APIs
- Prompt engineering and grounding for image and audio inputs
- Document intelligence: OCR, layout parsing, and visual Q&A
- Audio pipelines: transcription, speaker diarisation, and speech-to-action
- Evaluation and benchmarking of multimodal outputs
- Cost, latency, and reliability trade-offs across providers
- Production patterns: batching, caching, and error handling for multimodal workloads
Modalité
Delivered over 3-5 days, either in-person or fully remote via video conference with shared cloud lab environments. Each module is approximately 60% hands-on coding and 40% instructor-led explanation and architecture discussion. Participants receive access to a shared GPU-enabled cloud workspace pre-loaded with API keys and starter notebooks. A private Slack or Teams channel is maintained for async support during and after the bootcamp. In-person delivery is available in major European cities; remote cohorts are capped at 16 participants to preserve interactivity.
Ce qui fait que ça marche
- Start with a single, well-scoped multimodal use case (e.g., invoice extraction) before generalising the architecture
- Build a dedicated evaluation dataset with labelled multimodal examples before going to production
- Assign an engineer with API integration experience as the internal champion to maintain provider SDK updates
- Instrument cost and token usage per modality from day one to avoid bill shock at scale
Erreurs fréquentes
- Treating multimodal prompting identically to text prompting — image and audio tokens have different cost structures and context window implications that catch teams off guard in production
- Skipping modality-specific evaluation: teams ship visual Q&A features without benchmarks and only discover failure modes after user complaints
- Picking a single provider without testing latency and accuracy trade-offs across GPT-4o, Gemini, and Claude for the specific use case
- Underestimating preprocessing complexity — raw images and audio almost always need normalisation, resizing, or chunking before model ingestion
Quand NE PAS suivre cette formation
This training is not the right fit for a team that has not yet shipped any LLM-based feature to production — they will struggle with the hands-on labs and would benefit more from a foundational LLM engineering workshop first.
Fournisseurs à considérer
Sources
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