sortifal/MLOps

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Alexis Bruneteau c9dbe70bdb Fix DVC pull to only fetch raw data

Changed dvc pull to specifically pull data/raw.dvc instead of all
outputs. The processed data and model files are generated by the
DVC pipeline (dvc repro), not pulled from remote storage.

This prevents errors about missing processed files that haven't
been generated yet.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

2025-10-01 18:52:16 +02:00

setup dvc

2025-09-30 17:03:15 +02:00

.gitea/workflows

Fix DVC pull to only fetch raw data

2025-10-01 18:52:16 +02:00

Add CI/CD pipeline, monitoring, and model training components for CS:GO MLOps platform

2025-09-30 16:14:56 +02:00

Add CI/CD pipeline, monitoring, and model training components for CS:GO MLOps platform

2025-09-30 16:14:56 +02:00

setup dvc

2025-09-30 17:03:15 +02:00

Add CI/CD pipeline, monitoring, and model training components for CS:GO MLOps platform

2025-09-30 16:14:56 +02:00

secrets and mlflow should now work

2025-10-01 17:35:13 +02:00

maybe maybe not

2025-10-01 15:04:13 +02:00

secrets and mlflow should now work

2025-10-01 17:35:13 +02:00

train fix

2025-09-30 17:04:43 +02:00

.coverage

test

2025-09-30 16:38:14 +02:00

.dvcignore

Initialize DVC

2025-09-30 15:48:38 +02:00

.gitignore

Configure DVC credentials explicitly in CI/CD pipeline

2025-10-01 18:45:29 +02:00

docker-compose.yml

Add CI/CD pipeline, monitoring, and model training components for CS:GO MLOps platform

2025-09-30 16:14:56 +02:00

dvc.yaml

maybe maybe not

2025-10-01 15:04:13 +02:00

params.yaml

maybe maybe not

2025-10-01 15:04:13 +02:00

poetry.lock

Add dvc-s3 dependency for S3/MinIO storage support

2025-10-01 17:44:24 +02:00

pyproject.toml

Add dvc-s3 dependency for S3/MinIO storage support

2025-10-01 17:44:24 +02:00

README.md

Add Prometheus client dependency and update README with project details

2025-09-30 16:23:29 +02:00

README.md

MLOps Project

This is an MLOps project for CSGO data analysis and model training.

Features

Data pipeline with Apache Airflow
Model training with PyTorch and scikit-learn
MLflow for experiment tracking
DVC for data versioning
Monitoring with Prometheus
FastAPI for API serving

Setup

Install dependencies:
```
poetry install
```

Run the data pipeline:

airflow dags unpause csgo_data_pipeline

Project Structure

dags/: Airflow DAGs
src/: Source code
models/: Trained models
data/: Data files
notebooks/: Jupyter notebooks
tests/: Test files
config/: Configuration files
docker/: Docker files
kubernetes/: Kubernetes manifests