FORMATION IA
Bootcamp Ingénierie Computer Vision
Construisez, entraînez et déployez des systèmes de vision par ordinateur prêts pour la production.
Ce qu'elle couvre
Un bootcamp pratique couvrant l'ensemble de la stack ingénierie en vision par ordinateur : du traitement d'image classique aux modèles de détection d'objets, segmentation, OCR et modèles vision-langage. Les participants entraînent des modèles sur des jeux de données réels, optimisent les pipelines d'inférence et déploient des systèmes supervisés en production. Le programme alterne sessions de coding en direct, projets guidés et revues par les pairs sur quatre à six journées intensives.
À l'issue, vous saurez
- Fine-tune a YOLO or DETR model on a custom dataset and evaluate it using COCO metrics
- Build an end-to-end OCR and document-parsing pipeline ready for production ingestion
- Export a trained CV model to ONNX, apply INT8 quantisation, and benchmark inference latency
- Integrate a vision-language model (CLIP or LLaVA) into an application via API or local deployment
- Set up a production monitoring dashboard tracking prediction drift and confidence degradation
Sujets abordés
- Classical image processing: convolutions, feature extraction, OpenCV fundamentals
- Object detection architectures: YOLO, DETR, Faster R-CNN training and fine-tuning
- Instance and semantic segmentation with Mask R-CNN and SAM
- OCR pipelines: Tesseract, PaddleOCR, and document layout parsing
- Vision-language models: CLIP, LLaVA, and GPT-4V API integration
- Inference optimisation: TensorRT, ONNX export, quantisation, and edge deployment
- MLOps for CV: data versioning with DVC, experiment tracking with MLflow, model registry
- Production monitoring: data drift detection, prediction confidence tracking, alerting
Modalité
Typically delivered in-person or live-remote over five to six full days, with roughly 70% hands-on coding and 30% instructor-led theory. Each participant requires a GPU-enabled environment (cloud credits provided or pre-configured notebooks on Colab Pro / AWS). Materials include slide decks, annotated Jupyter notebooks, reference datasets, and a private GitHub repository. A capstone project—training and deploying a CV system on a participant-chosen use case—is presented on the final day.
Ce qui fait que ça marche
- Bring a real internal dataset and use case so the bootcamp capstone has immediate business relevance
- Pair each engineer with a GPU environment from day one to avoid environment setup delays
- Establish model evaluation baselines before fine-tuning to measure actual improvement
- Schedule a 30-day follow-up review session to consolidate production deployments and address blockers
Erreurs fréquentes
- Training on unbalanced or unlabelled datasets without establishing a data-quality baseline first
- Skipping inference optimisation and shipping full FP32 models to production, causing latency issues
- Treating vision-language models as drop-in replacements without evaluating hallucination rates on domain-specific images
- Neglecting post-deployment monitoring, leading to silent model degradation as input distributions shift
Quand NE PAS suivre cette formation
This bootcamp is not the right fit for a team that needs to evaluate whether computer vision is viable for their use case — they need a scoping workshop first. It is also unsuitable for data scientists who lack Python engineering skills, as the pace assumes engineering fluency.
Fournisseurs à considérer
Sources
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