Powered by AutoGS AI & AWS SageMaker
This solution includes:
- Wireframe Design
- Prototype
- Mockup Design
- SageMaker AI Model Update
- Development Requirements
- Getting Started
- Contribution Guidelines
- Code of Conduct
- Contact
- License
- Resources
- Training: Uses SmallSat earth observation data, processed via
imagery_processing.ipynb
- Model Registry: Tracks and versions trained models with full audit logs
- Deployment: Real-time inference through SageMaker Endpoints connected to the dashboard
- AutoML Option: Supports H2O AutoML for automated model selection
ml/carbon_model_sagemaker_pipeline.ipynb
: Defines the SageMaker ML pipelineml/deploy_endpoint.py
: Deploys models to SageMaker Endpointsml/inference_handler.py
: Handles API requests from the frontend
- Amazon SageMaker
- AWS Ground Station
- Amazon S3
- AWS Lambda
- Amazon CloudWatch
- Managed scalability
- Built-in versioning
- Secure infrastructure
- Reduced operational overhead
- Python 3.9+
- AWS CLI configured (IAM permissions required)
- AWS SDKs:
boto3
,sagemaker
- Python packages:
numpy
,pandas
,scikit-learn
,matplotlib
,transformers
torch
,tensorflow
,gradio
,rasterio
,cv2
sagemaker
,boto3
- Optional: Docker (for local SageMaker testing)
- Git client
- Clone the repository:
git clone https://github.com/aimtyaem/autogs.git
cd autogs
2. Set up Python environment:
python -m venv venv
source venv/bin/activate # Windows: venv\Scripts\activate
pip install -r requirements.txt
3. Configure AWS credentials:
aws configure
4. Run SageMaker pipeline notebook:
Open ml/carbon_model_sagemaker_pipeline.ipynb and execute cells.
Deploy endpoint with deploy_endpoint.py.
5. Launch frontend and connect to live AI models.
Contribution Guidelines
Code of Conduct
Contact
Ahmed Ibrahim Metawee
License
Licensed under the MIT License.