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Deploy State-of-the-Art Vision Models from GPU Lab to Edge Device

12 Weeks. Live Online Classes. Instructor-led.

Our Partners

Push to edge GPUs or Jetson devices with KServe + KEDA autoscaling

Train cutting-edge detection & segmentation models (YOLO-v8, Mask RCNN)

Compress, optimise & serve with ONNX Runtime and NVIDIA Triton

What you will learn?

    • Best-practice labelling, augmentation & synthetic data generation

    • Reproduce YOLO-v8 detection baseline; evaluate mAP, FPS

    • Fine-tune Mask RCNN / DeepLab-v3+ on custom classes

    • Apply mixed-precision & gradient-accumulation to fit big models on modest GPUs

    • Export to ONNX, TensorRT, and OpenVINO; prune & quantise to INT8

    • Benchmark latency vs accuracy trade-offs; hit target FPS/Watts budget

    • Wrap models in NVIDIA Triton with gRPC; add FastAPI gateway and JWT auth

    • Deploy to KServe; build Helm charts for Jetson / Orin devices; configure KEDA for GPU auto-scaling

    • Capture inference metrics via Prometheus; create Grafana CV dashboards

    • Detect data drift with Evidently-CV; auto-trigger retraining pipelines in Argo Workflows

    • Team project: quality-inspection, safety-monitoring, or inventory-tracking solution

    • Deliver live edge demo, cost analysis, and 8-page design document to hiring-manager panel

Go beyond the notebook and put vision models where they matter: in the factory, on the drone, at the edge. Starting with dataset curation and YOLO-v8 fine-tuning, you’ll compress models with ONNX/TensorRT, serve them through NVIDIA Triton, and deploy to Jetson or KServe with auto-scaling GPUs. The capstone—an end-to-end detection or segmentation system—gives you portfolio proof for Computer-Vision or Edge-AI roles in manufacturing, mining, retail, and robotics.

Who Should Enrol?

  • You already train image models and now need to deploy object-detection or segmentation services that hit latency and GPU-cost targets.

  • Level-up from tabular models to large-scale visual data pipelines, augmentation strategies and active-learning loops.

  • Build perception stacks that run reliably on edge devices and stream telemetry to the cloud for re-training.

  • Learn how to host heavy vision models behind REST/GRPC gateways, scale them with autoschedulers and cache results.

  • Gain the vocabulary and architectural patterns to scope, cost and roadmap vision features for mobile, web or embedded products.

Engineers who must bring computer-vision models all the way to production hardware.

Prerequisites
Solid Python, basic deep-learning knowledge. Free Deep Learning Core + Docker-K8s Mini-Camp bridge badges are included for all Intermediate-track graduates.

Career Pathways

  • Design, train and deploy classification, detection and segmentation models at scale.

  • Optimise and quantise models for mobile, IoT and robotics hardware with TensorRT or ONNX Runtime.

  • Automate data versioning, model registry, CI/CD and real-time monitoring for high-throughput image pipelines.

  • Integrate perception models with control loops for drones, vehicles or industrial robots.

  • Scope end-to-end vision systems, balance cloud vs. edge costs, and ensure GDPR/ethics compliance.

Graduates leave with a fully-containerised vision service running on cloud & edge, a public GitHub repo, and recruiter-ready talking points that match the majority of “Computer-Vision Engineer” and “Edge-AI Engineer” roles advertised today.

Amir Charkhi
Technology leader | Adjunct Professor | Founder

With 20 + years across energy, mining, finance, and government, Amir turns real-world data problems into production AI. He specialises in MLOps, cloud data engineering, and Python, and now shares that know-how as founder of AI Tech Institute and adjunct professor at UWA, where he designs hands-on courses in machine learning and LLMs.

Advanced: Computer Vision @ Scale

12 Weeks. Live Online Classes. Next Cohort 2nd September

Advanced: Computer Vision at Scale Advanced: Computer Vision at Scale
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Advanced: Computer Vision at Scale
$4,250.00

  • “In just six weeks I went from zero Python to building a churn-prediction model I could demo at work. I finally feel job-ready.” — Maddie, Data Analyst

  • “The pace was perfect—hands-on from day one and always tied back to real business value. Best learning experience I’ve had online.” — Demian, Mining Engineer

  • “Being a new mum, flexible recordings were a lifesaver. I could pause, practice, and still keep up with the cohort.” — Molly, Graduate Analyst

  • “Clear path: beginner foundations, then straight into their Intermediate Data Science track. No other provider lays out the journey so cleanly.” — Bec, Business Analyst

  • “Projects mirrored the stack we use at work—no toy examples. I deployed my capstone on AWS the same night.” — Amy, Cloud Architect

  • “From pandas to production MLOps in 24 weeks—and I never felt lost. The Slack support channel replies within minutes.” — Arjun, Energy Sector Intern

Frequently Asked Questions

  • Beginner courses: none— we start with Python basics.
    Intermediate & Advanced: ability to write simple Python scripts and use Git is expected.

  • Plan on 8–10 hours: 2× 3-hour live sessions and 2–4 hours of project work. Advanced tracks may require up to 10 hours for capstone milestones.

  • All sessions are recorded and posted within 12 hours. You’ll still have access to Slack/Discord to ask instructors questions.

  • New intakes launch roughly every 8 weeks. Each course page shows the exact start date and the “Apply-by” deadline.

  • Just a laptop with Chrome/Firefox and a stable internet connection. All coding happens in cloud JupyterLab or VS Code Dev Containers—no local installs.

  • Yes. 100 % refund until the end of Week 2—no questions asked. After that, pro-rata refunds apply if you need to withdraw for documented reasons.

  • Absolutely. We issue invoices to companies and offer interest-free 3- or 6-month payment plans.

  • Live Q&A in every session, 24-hour Slack response time from instructors, weekly office-hours, and code reviews on your GitHub pull requests.