How to Build an MLOps Pipeline: From Data Management to Deployment
Discover the journey of building an MLOps pipeline! In this project, I've completed half of my internship task, leveraging tools like Kedro, EvidentlyUI, and DVC with Google Drive integration to manage raw and live datasets. With MongoDB storing the live dataset, updated daily, I'm now gearing up to incorporate MLflow, DagsHub, and Docker for streamlined deployment in the cloud. Success in this endeavor means achieving all five parameters of an MLOps pipeline.
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