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ClassiSage: Terraform IaC Automated AWS SageMaker based HDFS Log classification Model
- ClassiSage is a machine learning model designed with AWS SageMaker and its Python SDK for log classification.
- The infrastructure setup of the project is automated using Terraform, a tool that provides infrastructure-as-code.
- The data set used is HDFS_v1, implementing SageMaker Python SDK with the XGBoost model version 1.2.
- The project can be run by following the Directory Structure mentioned on the ClassiSage project repository uploaded on GitHub.
- Console Observations include changes in instances and infrastructure that can be observed while running the project.
- Auto-created objects include Files and Folders created during the execution process.
- The code is executed through Jupyter Notebook on AWS SageMaker and the dataset is stored in S3.
- The overall process includes setting up an output path, initializing hyper parameters, running the training job, deploying it, and validation.
- The end-point and the resources within the S3 bucket should be deleted to ensure no additional charges.
- The project repository can be checked on GitHub, and star-rated if the implementation is liked.
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