Summary

Experiment Tracking

An MLflow server hosted on Amazon SageMaker provides centralized experiment tracking and artifact management. Provisioning through Terraform ensures the setup is consistent, repeatable, and easy to maintain across stages.

Ready‑to‑Use Training Environment

An one‑click Jenkins pipeline delivers ready‑to‑work SageMaker notebook environment. On‑create and on‑start lifecycle configurations preinstall dependencies and bootstrap settings so users can begin immediately without manual setup.

Local‑to‑Cloud Continuity

Development starts wherever it’s most convenient—often on a local machine. Code and data are pushed to a remote repository and pulled into the cloud notebook, ensuring consistent results and a frictionless transition between environments.

Data Management and Lineage

Data Version Control (DVC) with Amazon S3 as the backend keeps datasets and artifacts versioned and trackable. This creates a clear lineage from raw data to trained models, aligning data states with experiments and deployments.

Automated Deployment

A Jenkins pipeline promotes and deploys the champion model automatically. This reduces manual intervention, enforces a reliable release process.