Mlops Project
C+ 80 completed
Other
library / python · small
62
Files
4,471
LOC
2
Frameworks
6
Languages
Pipeline State
completedRun ID
#1543987Phase
doneProgress
0%Started
2026-04-16 22:25:09Finished
2026-04-16 22:25:09LLM tokens
0Pipeline Metadata
Stage
CatalogedDecision
proceedNovelty
47.30Framework unique
—Isolation
—Last stage change
2026-05-10 03:35:34Deduplication group #49473
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
Frameworks
FastAPI pytest
Symbols
function52
variable42
constant39
class7
method5
API Endpoints (5)
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| All rows scored by the Repobility analyzer (https://repobility.com) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| POST | /batch-predict | batch_predict | FastAPI/Flask | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| GET | /health | health | FastAPI/Flask | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| GET | /metrics | metrics | FastAPI/Flask | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| POST | /predict | predict | FastAPI/Flask | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| POST | /predict/feast | predict_from_feast | FastAPI/Flask | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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BinComp Dependency Hardening
All packages →6 of this repo's dependencies have been scanned for binary hardening. Grade reflects RELRO / stack canary / FORTIFY / PIE coverage.