This walkthrough includes the following steps:
- Collect and prepare your own dataset to feed into an ML algorithm
- Train a model with Amazon SageMaker, a fully managed service that provides the ability to build, train, and deploy machine learning (ML) models quickly
- Running the model locally on AWS DeepLens to predict types of trash without sending any data to the cloud
- Optionally, after AWS DeepLens makes its prediction, you can set up AWS DeepLens to send a message to a Raspberry Pi via AWS IoT Greengrass to show you which bin to throw the item in. The following diagram illustrates the solution architecture.