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README
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Developed by: Alejandro Henao
Date: 01-May-2022
Repo composed of python files for the development of a web app using Streamlit using a pretrained model inside S3 and deploying an
Endpoint with Sagemaker for on demand REQUESTS of the API.
Proposed work if i had more time:
- Develop streamlit app inside EC2
- Use the secrets Github functionality for AWS credentials
- Upload Docker image to Dockerhub for dependencies, this would help to upload to ECR and be able to run app inside EC2.
Conclusions:
Developing the streamlit app inside EC2 would prove to be a better solution due to not running on local, however EC2 instances are very high cost.
AWS Sagemaker is pretty slow and costly for on demand, probably AWS Lambda might be a better solution.
Streamlit is pretty limmitted and has only linear workflow conditions, Flask could prove to be a more robust micro web framework solution.
XGBoost is incredibly good for tabular data for very low training time and hyperparameter tuning.
NOTE: CODE IS NOT FUNCTIONAL, access keys are deactivated.