Computer Vision for Builders
Learn to integrate computer vision APIs into real products: image generation, OCR, multimodal models, object detection, and production pipelines — using the same tools that power modern AI applications.
What you'll learn
Course outline
Free — no account needed
Computer Vision APIs — What They Can Do Without a PhD
The full landscape of computer vision tasks, which API to use for each, and what things cost
Image Generation APIs — DALL-E, Stability AI, and Flux
Generate, edit, and transform images programmatically using the leading generation APIs
OCR and Document Intelligence — Extracting Text from Images
Extract structured data from receipts, invoices, and scanned documents using cloud APIs
Full course — $59 one-time
Multimodal Models — Sending Images to Claude and GPT-4o
Combine text and image inputs to build powerful analysis, moderation, and extraction pipelines
Image Classification and Object Detection
Use pre-trained models via API and train custom detectors with Roboflow
Building an Image Analysis Pipeline
Upload → storage → processing → structured output → database — end to end
Production Considerations for Computer Vision
Rate limits, batching, caching, content safety, and GDPR for image processing pipelines
Get the full course
7 lessons — practical, project-based, no fluff.
About this course
Computer vision gives software the ability to understand images and video — identifying objects, reading text, detecting faces, segmenting scenes, and now reasoning about visual content in natural language. Until recently, building computer vision applications required deep ML expertise. Today, pre-trained models via Hugging Face, cloud vision APIs from Google and AWS, and multimodal LLMs like GPT-4o and Claude make it possible for any developer to add vision capabilities to their product. This course teaches you to use the right tool for each vision task — without training models from scratch.
The course covers the full spectrum: simple classification and OCR tasks where a cloud API is the right answer, object detection where you need bounding boxes and confidence scores, semantic segmentation for complex scene understanding, and vision-language models for tasks that require reasoning rather than just recognition. You will build and deploy a real computer vision feature by the end of the course.
Frequently asked questions
Do I need a GPU to build computer vision applications?
For inference (running a pre-trained model to process images), most modern hardware is fast enough — CPUs can run lightweight models like MobileNet or YOLO-Nano at reasonable speed. For production-grade object detection on video streams or high-throughput image processing, a GPU-enabled server (AWS p3, Google Cloud T4, or a RunPod serverless GPU) is needed. For the LLM-based vision APIs (GPT-4o, Claude Vision), you call an API and pay per image — no GPU needed at all.
What is the difference between image classification and object detection?
Image classification assigns a single label to the whole image: "this is a photo of a cat." Object detection finds every instance of objects in an image and draws bounding boxes around each one with a confidence score: "cat at (120, 80, 200, 180) with 94% confidence, dog at (300, 50, 420, 200) with 88% confidence." Classification is simpler and faster; detection is needed when you need to locate objects or when multiple objects of interest may appear in one image.
When should I use a vision-language model versus a traditional CV model?
Use a vision-language model (GPT-4o, Claude Vision, Gemini) when: the task requires reasoning or description ("describe what is wrong with this invoice", "is this food safe for someone with a nut allergy"), the output is open-ended text, or you need flexibility across many visual tasks without specialised models. Use traditional CV models (YOLO, ResNet, EfficientNet) when: you need high-throughput (thousands of images per second), low latency, or very high accuracy on a narrow task where a fine-tuned model outperforms general models.
What is OCR and when is it good enough?
OCR (Optical Character Recognition) converts images of text into machine-readable text. Cloud OCR APIs (Google Cloud Vision, AWS Textract, Azure Form Recognizer) handle standard documents, receipts, and forms well and are cheap per page. They struggle with: handwriting, unusual fonts, tables with complex structure, and multi-language mixed documents. For structured document extraction (invoices with field-level values), Azure Form Recognizer and AWS Textract are better than generic OCR — they extract labelled fields, not just raw text.
How do I handle privacy when processing images that contain people?
Key considerations: only process images you have legal authority to process (user-uploaded images with consent, your own cameras with signage). If sending images to third-party APIs (OpenAI, Google), review their data retention policies — most allow you to opt out of training use. For face detection and recognition specifically, many jurisdictions have additional legal requirements (GDPR biometric data rules, Illinois BIPA). When in doubt, blur or crop faces before processing or storing images.