AI TRAINING
Computer Vision Engineering Bootcamp
Build, train, and deploy production-grade computer vision systems from detection to multimodal models.
What it covers
A hands-on bootcamp covering the full computer vision engineering stack: from classical image processing through modern object detection, segmentation, OCR, and vision-language models. Participants train models on real datasets, optimise inference pipelines, and deploy monitored production systems. The programme combines live coding sessions, guided projects, and peer reviews across approximately four to six intensive days of instruction.
What you'll be able to do
- 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
Topics covered
- 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
Delivery
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.
What makes it work
- 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
Common mistakes
- 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
When NOT to take this
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.
Providers to consider
Sources
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