jithsaavvy / deploying-an-end-to-end-keyword-spotting-model-into-cloud-server-by-integrating-ci-cd-pipeline Goto Github PK

• 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.

When using poetry over pip, it is possible to add and install dependencies using poetry add. But, what if there are multiple dependencies specified in requirements.txt (conventional way) ?