Key takeaways
- Computer vision lets machines understand images and video — detection, classification, and recognition.
- AWS offers services and free training for building vision applications.
- Beginners can start with fundamentals before diving into code.
- It powers self-driving, medical imaging, retail, and security.
- Related: free cloud computing courses and how to start learning AI.
Computer vision tasks
| Task | What it does | Example |
|---|---|---|
| Classification | Labels an image | Cat vs dog |
| Detection | Finds objects | Boxes around cars |
| Recognition | Identifies specifics | Faces, text (OCR) |
| Segmentation | Pixel-level regions | Medical scans |
This course provides an overview of Computer Vision (CV), Machine Learning (ML) with Amazon Web Services (AWS), and how to build and train a CV model using the Apache MXNet and GluonCV toolkit. The course discusses artificial neural networks and other deep learning concepts, then walks through how to combine neural network building blocks into complete computer vision models and train them efficiently.
This course covers AWS services and frameworks including Amazon Rekognition, Amazon SageMaker, Amazon SageMaker Ground Truth, and Amazon SageMaker Neo, AWS Deep Learning AMIs via Amazon EC2, AWS Deep Learning Containers, and Apache MXNet on AWS. The course is comprised of video lectures, hands-on exercise guides, demonstrations, and quizzes.
Each week will focus on different aspects of computer vision with GluonCV. In week one, we will present some basic concepts in computer vision, discuss what tasks can be solved with GluonCV and go over the benefits of Apache MXNet.
In the second week, we will focus on the AWS services most appropriate to your task. We will use services such as Amazon Rekognition and Amazon SageMaker. We’ll review the differences between AWS Deep Learning AMIs and Deep Learning containers. Finally, there are demonstrations on how to set up each of the services covered in this module.
Week three will focus on setting up GluonCV and MXNet. We will look at using pre-trained models for classification, detection and segmentation.
During week four and five, we will go over the fundamentals of Gluon, the easy-to-use high-level API for MXNet: understanding when to use different Gluon blocks, how to combine those blocks into complete models, constructing datasets, and writing a complete training loop.
In the final week, there will be a final project where you will apply everything you’ve learned in the course so far: select the appropriate pre-trained GluonCV model, apply that model to your dataset and visualize the output of your GluonCV model.
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Frequently asked questions
What is computer vision?
Computer vision is the field of AI that enables machines to interpret images and video — classifying images, detecting and recognizing objects, and segmenting scenes.
Can I learn AWS computer vision for free?
Yes. AWS Skill Builder offers free training, and AWS provides managed vision services plus free-tier options to practice building applications.
Do I need to code for computer vision?
For custom models, yes (usually Python). But managed cloud services let you use pre-built vision capabilities with minimal code to start.
What is computer vision used for?
Self-driving cars, medical imaging, retail checkout, security and surveillance, quality inspection, and document/text recognition (OCR), among many others.
How do I start learning computer vision?
Begin with the fundamentals (how images and models work), try a cloud vision service on sample images, then learn Python and a framework to build custom models.
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