https://github.com/chukwuemekaaham/aws-ml-classification-task
This notebook demonstrates how to leverage transfer learning to use your own image dataset to build and train an image classification model using MXNet and Amazon SageMaker.
https://github.com/chukwuemekaaham/aws-ml-classification-task
aws aws-iot deeplens lambda mxnet python sagemaker sagemaker-deployment
Last synced: 2 months ago
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This notebook demonstrates how to leverage transfer learning to use your own image dataset to build and train an image classification model using MXNet and Amazon SageMaker.
- Host: GitHub
- URL: https://github.com/chukwuemekaaham/aws-ml-classification-task
- Owner: ChukwuemekaAham
- Created: 2021-09-27T17:27:08.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-08-13T06:54:34.000Z (over 2 years ago)
- Last Synced: 2025-01-10T00:17:00.206Z (4 months ago)
- Topics: aws, aws-iot, deeplens, lambda, mxnet, python, sagemaker, sagemaker-deployment
- Language: Jupyter Notebook
- Homepage:
- Size: 145 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# aws-ml-classification-task
This walkthrough includes the following steps:
1. Collect and prepare your own dataset to feed into an ML algorithm
2. Train a model with Amazon SageMaker, a fully managed service that provides the ability to build, train, and deploy machine learning (ML) models quickly
3. Running the model locally on AWS DeepLens to predict types of trash without sending any data to the cloud
4. 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.