Computer Vision Engineering 100 Production-Grade Labs

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Master OpenCV image and video processing, including color spaces, transformations, filtering, contours, and real-time stream handling.
Build and train production-oriented PyTorch classifiers using custom datasets, transfer learning, validation strategies, and secure model management.
Architect and deploy real-time object detection systems using YOLO, bounding-box mathematics, NMS, FPNs, mAP evaluation, and custom datasets.
Engineer semantic and instance segmentation pipelines using U-Net, Mask R-CNN, and modern vision foundation models such as SAM.
Build real-time video analytics systems with optical flow, Kalman filtering, DeepSORT, multi-camera Re-ID, action recognition, RTSP ingestion, and asynchronous
Optimize deep learning inference from FP32 to INT8 using ONNX and TensorRT while profiling CPU/GPU performance, memory usage, batching, and latency.
Deploy containerized computer vision workloads with Docker, Kubernetes, Triton Inference Server, CI/CD pipelines, model versioning, data-drift detection, and au
Engineer production observability with Prometheus, Grafana, OpenTelemetry, structured logging, telemetry, failure-case capture, and model-degradation alerting.
Secure and govern computer vision platforms using RBAC, TLS, threat modeling, PII anonymization, GDPR principles, EU AI Act risk considerations, adversarial-def
Architect and deliver a PhD-level sovereign edge-AI capstone capable of processing multi-stream RTSP video with optimized inference, high availability, observab
Requirements

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Minimum Technical Requirements

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1. Python 3.12 recommended.
2. Git and GitHub/GitLab familiarity is helpful but not required.
3. No previous professional Computer Vision experience is required.
Recommended Hardware
1. Modern quad-core or better CPU
2. 16 GB RAM minimum
3. 32 GB RAM recommended
4. 50-100 GB available SSD storage
Description

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This course contains the use of artificial intelligence.I only charge a fee solely for the time invested in building this comprehensive curriculum.Stop Vibe Coding. Start Engineering Computer Vision Systems.There is a huge difference between getting a computer vision model to run and engineering a computer vision system that can survive production.Vibe coding can get you a demo.A few lines of Python can open a camera, detect a person, draw a bounding box, and produce an impressive .But what happens when the camera produces an RTSP stream 24/7?What happens when inference must stay below 50ms?What happens when GPU memory becomes the bottleneck?What happens when your model drifts?What happens when a Kubernetes node crashes?What happens when personally identifiable information enters your logs?What happens when your organization needs GDPR controls, EU AI Act risk management, audit trails, RBAC, encryption, and zero external data leakage?That's where engineering begins.This course is built around that exact transition:From Vibe Coding → Production-Grade Computer Vision Engineering.Instead of spending 100 lessons jumping randomly between disconnected tutorials, you will progress through 100 structured technical labs, with each stage building the capabilities required for the next.Your 100-Lab Engineering JourneyYou start at the foundation.Module 1 - Build the Vision FoundationYou will configure your Linux environment and learn how images actually behave as data.You will work with OpenCV matrices, color spaces, histogram equalization, geometric transformations, Canny and Sobel edge detection, contours, video capture, and automated frame verification.This isn't just "learning OpenCV."You are establishing the computational foundation required for everything that follows.Module 2 - Enter Deep LearningYou will move into PyTorch tensors, autograd, custom datasets, dataloaders, convolutional layers, loss functions, optimization, validation, transfer learning, and fine-tuning.By the end, you will have built a serious image-classification pipeline rather than simply calling a pre-trained model.Module 3 - Engineer Object DetectionNow the system becomes significantly more sophisticated.You will understand bounding boxes, IoU, NMS, SSD architectures, YOLO training, anchor optimization, feature pyramids, mAP evaluation, and class imbalance.The objective is not merely to run YOLO.The objective is to understand the engineering decisions behind a robust detector.Module 4 - Segmentation & Vision Foundation ModelsYou will progress from "what object is this?" to "exactly which pixels belong to it?"You will build U-Net and Mask R-CNN pipelines, explore zero-shot detection, promptable segmentation, vision-language embeddings, automated annotation with SAM, open-vocabulary detection, and video tracking.Module 5 - Build Real-Time Video IntelligenceStatic images are only the beginning.You will engineer systems capable of understanding continuous video using optical flow, background subtraction, Kalman filtering, DeepSORT, identity re-identification, multi-camera tracking, zone analytics, action recognition, RTSP streams, and asynchronous processing queues.This is where Computer Vision begins behaving like a real operational system.From Models to High-Performance Edge AIModules 6 and beyond focus on a critical industry requirementerformance.A model that achieves excellent accuracy but cannot meet production latency requirements is not a production solution.You will transform models through:FP32 → INT8 quantizationONNX exportTensorRT engine constructionCalibrationWeight pruningCPU/GPU benchmarkingC++ inference wrappersMultithreaded executionMemory profilingDynamic batchingThe goal is simple:Take intelligence and make it fast enough to operate in the real world.Then Build the Production PlatformNext, you move from individual models to infrastructure.You will containerize your workloads with Docker, reduce image sizes using multi-stage builds, scale inference with Kubernetes, configure Triton Inference Server, implement model versioning, establish CI/CD testing, detect data drift, trigger retraining, and safely A/B test inference endpoints.At this point, you are no longer simply an ML practitioner.You are engineering an AI platform.Make Your System ObservableProduction systems need evidence.You will implement:Structured JSON loggingPrometheus metricsGrafana dashboardsOpenTelemetry tracingGPU/RAM monitoringLatency analysisFailed-inference captureModel degradation alertsAudit loggingYou will learn to answer questions such as:Why did inference slow down?Which component is creating the bottleneck?Is the model degrading?Did the GPU become saturated?Can we prove what happened during an incident?That's the difference between deploying software and operating software.Security, Privacy & GovernanceComputer vision can process some of the most sensitive data an organization owns.Therefore, security cannot be an afterthought.You will engineer threat models, secure REST/gRPC inference APIs, RBAC, TLS, face-blurring and PII masking pipelines, GDPR-oriented workflows, EU AI Act risk considerations, adversarial attack simulations, model watermarking, and encrypted dataset storage.The objective is to build systems that are not merely intelligent-but defensible, auditable, and secure.The Climax: Lab 100 - The PhD-Level CapstoneEverything culminates in Lab 100: Autonomous Edge Surveillance Grid.This isn't another toy project.You will architect a production-style autonomous edge computer vision platform that ingests multiple live RTSP video streams, performs optimized TensorRT inference and object tracking, identifies behavioral anomalies, and publishes operational telemetry to a Kubernetes-managed dashboard.The architecture is designed around sovereign deployment principles:No unnecessary external data leakage.Local processing.High availability.Security controls.Real-time observability.Edge acceleration.Resilience under failure.Your final system is evaluated against real engineering criteria, including:≥30 FPS real-time inference per streamSub-50ms processing latencyAutomated recovery from simulated infrastructure failuresStrict RBAC enforcementVulnerability scanningOn-device facial-feature redactionReal-time Grafana operational dashboardsDrift and inference-health monitoringThis is the point where the 100 labs converge into one architecture.You don't simply finish a course.You demonstrate that you can design, optimize, secure, observe, and operate a serious Computer Vision platform. This Course Is About Engineering, Not MemorizationYou will not be asked to blindly copy notebooks and hope they work.You will progressively learn why systems are designed the way they are, how components interact, where failures occur, and how to make computer vision workloads faster, safer, observable, reproducible, and deployable.By the final lab, you will have traveled fromixels → Tensors → Models → Detection → Tracking → Optimization → Containers → Kubernetes → Observability → Security → Sovereign Edge AIThat is the engineering journey.Why Enroll Now?The industry is moving beyond engineers who can simply train models.The valuable skill is increasingly the ability to take AI from prototype to reliable production system.If you want to build that capability systematically-with 100 hands-on labs instead of fragmented tutorials-this course gives you the roadmap, implementation practice, and final architecture challenge.Start with Lab 1.Build the foundations.Break things.Measure them.Optimize them.Secure them.Deploy them.Then build your own production-grade Computer Vision system.Your journey from Vibe Coder to Computer Vision Engineer starts here.
1- The Aspiring AI / Computer Vision Engineer,You can train a model in a notebook, but you want to understand what happens after training.,2- The Software/MLOps Engineer Entering AI,You already understand software engineering, DevOps, containers, APIs, or cloud infrastructure-but computer vision has been your missing piece.,3- The Senior Engineer Building Sovereign AI Infrastructure,You care about more than model accuracy. You need systems that can operate under strict privacy, security, latency, reliability, and infrastructure constraints-without depending entirely on external cloud services.
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