serverless-offline VS OpenCV

Using local emulators like sam local, serverless-offline, localstack, etc.

Hi, I'm the creator of serverless-offline (https://github.com/dherault/serverless-offline), a NPM package for local serveless development on AWS.

I'm building a cool product and intend to launch Q1 2023. To gain traction from my target customers, I plan to place a discrete ad at the launch of every serverless-offline instance.

Does it seem like an ok move to you? Or is it something that repels you?

Best,

As the creator of serverless-offline, I am well placed to tell you that this is the time-effective solution. Plus, it costs way less than other solutions at smaller scales. But, again, going Kubernetes or otherwise will be a problem for the future.

https://github.com/dherault/serverless-offline https://github.com/lambci/docker-lambda

What if I didn’t need to push code to AWS every time I wanted to test something? All heroes don’t wear capes. Like a knight in shining armor, Serverless Offline comes barging in to save the day! At least now I can test all my code locally before pushing it to AWS. That’s a relief.

With classic Express servers, you can use a simple node script to get the server up and running to test locally. Serverless wants to be run in the AWS ecosystem making it. Lucky for us, David Hérault has built and continues to maintain serverless-offline allowing us to emulate our functions locally before we deploy.

With the serverless offline plugin you can speed up local dev is by emulating AWS lambda and API Gateway locally when developing your Serverless project.

Those mocks are by definition not real services so there are some behavior differences between the local environment and the cloud provider. For example, AWS API Gateway emulated by serverless-offline doesn't handle VTL locally the same as AWS does.

serverless-offline (https://github.com/dherault/serverless-offline) is a great tool to use for local development of serverless applications.

It's not a complete mirror image of what you get but it's close enough in my experience.

Referenceshttps https://www.kaggle.com/datasets/riyaelizashaju/skin-disease-image-dataset-balanced?fbclid=IwAR3wbTp8l5yo_5fx6HAX8Vd2-9cca3khAc8EiBGFObaALfdVid29IuB_rYE https://keras.io/api/applications/vgg/ https://www.tensorflow.org/tutorials/images/cnn?hl=th https://opencv.org/

Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks.

You'll need to plot the predictions. There are a few open source tools to do that, supervision is one you can use (https://github.com/roboflow/supervision) and opencv is another common option (https://github.com/opencv/opencv)

You might be able to achieve this with scripting tools like AutoHotkey or Python with libraries for GUI automation and image recognition (e.g., PyAutoGUI https://pyautogui.readthedocs.io/en/latest/, OpenCV https://opencv.org/).

The OpenCV4NodeJs A.I. library provides an interface for calling OpenCV routines in NodeJS.