AWS Lambda + OpenCV
Incorporating OpenCV into AWS Lambda + DynamoDB + S3 Bucket system
Description
Interested in creating your own API? Do you want to deploy solutions for AI, image processing, and more? Well-made APIs can be used as company internal functions as well as commercially available.
In this lecture, we aim to implement a simple image processing system, and learn AWS functions, which are necessary elements for implementation. (S3 Bucket, Lambda, DynamoDB). By combining these concepts with OpenCV, an image processing Python package, you can easily understand the basic concepts of AWS Serveless System through practical examples.
What You Will Learn!
- How can an image processing function like OpenCV be made into an end-to-end system?
- I want to take the first step in learning the various skills needed to implement an AWS serverless system.
- I hope I can easily understand how to extend the OpenCV implementation result to API...
- Incorporating OpenCV into AWS Lambda + DynamoDB + S3 Bucket system
- This is a lecture that introduces serverless systems through AWS Lambda and expands them to API by creating an image processing end system with OpenCV, an image
Who Should Attend!
- Wouldn't it be really attractive if an image processing library like OpenCV could be served as a Lambda? There are so many things you can do. By introducing the process of servicing the OpenCV library as Lambda, I would like to show that it can be applied to anything else.
- AWS Lambda was born to serve lightweight systems under 50MB. Therefore, it is difficult to service AI Inference as it is, and it can be serviced through lightweighting such as Tensorflow-lite, PyTorch Jit, or ONNX.
- It is almost free to use through the free-tier. Even if there is no free-tier, you pay for what you use.