Future steps

[Jithsaavvy]

• Implement data management pipeline for data extraction, validation, data version control etc.
• Use cloud storage services like Amazon S3 bucket to store data, artifacts, predictions.
• Even though exception handling is implemented in the code, it is equally important to write separate test cases for different scenarios.
• Orchestrate the entire workflow as automated pipeline by means of orchestration tools like Airflow, KubeFlow. As this is a small personal project with a static dataset, the pipeline can be created using normal function calls. But, it is crucial and predominant to replace them with orchestration tools for large, scalable and real-time workflows.
• Implement Continuous Training (CT) pipeline along with CI/CD.

[Jithsaavvy]

Added test cases using pytest and automated a new workflow which runs all tests for every commit.

Future steps - remaining

• Implement data management pipeline for data extraction, validation, data version control, etc.
• Use cloud storage services like Amazon S3 bucket to store data, artifacts, and predictions.
• Orchestrate the entire workflow as an automated pipeline by means of orchestration tools like Airflow, and KubeFlow. As this is a small personal project with a static dataset, the pipeline can be created using normal function calls. But, it is crucial and predominant to replace them with orchestration tools for large, scalable, and real-time workflows.
• Implement Continuous Training (CT) pipeline along with CI/CD.