01 / MLOps / Data Science / May 2026
Credit Scoring MLOps
Credit scoring MLOps project with automated preprocessing, MLflow experiment tracking, DagsHub model registry, CI workflow, Podman runtime, and Grafana monitoring evidence.
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Overview
Credit Scoring MLOps is an end-to-end MLOps case study for a machine learning credit scoring workflow.
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Challenge
A credit scoring model needs more than a training script to feel production-aware.
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Outcome
The result is a compact MLOps project that demonstrates operational discipline around machine learning: reproducible setup, tracked experiments, registry-backed artifacts, CI proof, and dashboard evidence for monitoring.
Project background
Why this project exists
Credit Scoring MLOps is an MLOps-oriented machine learning project built around a practical scoring workflow. The project is less about a single model result and more about the operational path around the model: preprocessing, experiment tracking, model registry, CI evidence, and monitoring proof.
The portfolio value comes from showing how machine learning work becomes reviewable and repeatable. MLflow, DagsHub, GitHub Actions, Podman, and Grafana turn the project from a notebook-style experiment into a lifecycle artifact that can be inspected from training to monitoring.
Build notes
How it was shaped
Automated preprocessing so the modelling workflow starts from a repeatable prepared dataset.
Used MLflow 2.19.0 with DagsHub tracking and model registry to preserve runs, artifacts, and model lineage.
Added GitHub Actions, Podman runtime notes, and Grafana monitoring evidence to make the project stronger as an MLOps portfolio artifact.