Mlops Project

C+ 80 completed
Other
library / python · small
62
Files
4,471
LOC
2
Frameworks
6
Languages

Pipeline State

completed
Run ID
#1543987
Phase
done
Progress
0%
Started
2026-04-16 22:25:09
Finished
2026-04-16 22:25:09
LLM tokens
0

Pipeline Metadata

Stage
Cataloged
Decision
proceed
Novelty
47.30
Framework unique
Isolation
Last stage change
2026-05-10 03:35:34
Deduplication group #49473
Member of a group with 28 similar repo(s) · framework fastapicanonical #1562196 view group →
About: code-quality intelligence by Repobility · https://repobility.com

AI Prompt

Create an end-to-end MLOps demo project for predicting customer churn using the Telco Customer Churn dataset. The system should be fully containerized using Docker Compose and include components for data versioning with DVC, orchestration via Airflow, experiment tracking with MLflow, and model serving using FastAPI. I also need to incorporate a Feature Store using Feast, model monitoring with Evidently AI, and observability using Prometheus and Grafana. The setup should be runnable with a single `docker-compose up` command, and I need to include steps for training the model and making predictions via the FastAPI endpoint.
python fastapi mlops airflow dvc mlflow feast docker monitoring eda
Generated by gemma4:latest

Catalog Information

Create an end-to-end MLOps demo project for predicting customer churn using the Telco Customer Churn dataset. The system should be fully containerized using Docker Compose and include components for data versioning with DVC, orchestration via Airflow, experiment tracking with MLflow, and model serving using FastAPI. I also need to incorporate a Feature Store using Feast, model monitoring with Evidently AI, and observability using Prometheus and Grafana. The setup should be runnable with a single

Tags

python fastapi mlops airflow dvc mlflow feast docker monitoring eda

Quality Score

C+
79.7/100
Structure
75
Code Quality
100
Documentation
58
Testing
75
Practices
65
Security
100
Dependencies
80

Strengths

  • CI/CD pipeline configured (github_actions)
  • Good test coverage (43% test-to-source ratio)
  • Consistent naming conventions (snake_case)
  • Low average code complexity — well-structured code
  • Good security practices — no major issues detected
  • Containerized deployment (Docker)

Weaknesses

  • No LICENSE file — legal ambiguity for contributors
  • 102 duplicate lines detected — consider DRY refactoring

Recommendations

  • Add a linter configuration to enforce code style consistency
  • Add a LICENSE file (MIT recommended for open source)

Languages

python
60.5%
yaml
22.9%
json
11.1%
markdown
4.8%
text
0.6%
shell
0.1%

Frameworks

FastAPI pytest

Symbols

function52
variable42
constant39
class7
method5

API Endpoints (5)

Source: Repobility analyzer (https://repobility.com)
MethodPathHandlerFramework
All rows scored by the Repobility analyzer (https://repobility.com)
POST/batch-predictbatch_predictFastAPI/Flask
GET/healthhealthFastAPI/Flask
GET/metricsmetricsFastAPI/Flask
POST/predictpredictFastAPI/Flask
POST/predict/feastpredict_from_feastFastAPI/Flask

Quality Timeline

1 quality score recorded.

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