Here you can find samples, tutorials, reference architectures and other resources to help you to build your own ML@Edge (Machine Learning at the Edge) solution at AWS.
| Use Case | Description |
|---|---|
| How to detect anomalies in "Wind Turbines" in real-time? | This sample provides an end-to-end solution that manages the lifecycle of an anomaly detection model, deployed to a simulated fleet of wind turbine |
| How to classify images in the browser? | This sample provides an end-to-end solution that manages the lifecycle of an image detection model, deployed to a local device (laptop, mobile) |
If you have a question related to a business challenge that must be answered by an accelerated AI/ML solution, like the content in this repo, then you can contribute. You can just open an issue with your question or if you have the skills, implement a solution (tutorial, workshop, etc.) using Jupyter notebooks (for SageMaker Studio or Notebook Instances) and create a pull request. We appreciate your help.
Please refer to the CONTRIBUTING document for further details on contributing to this repository.