Added input_example parameter to auto-infer model signature and explicitly set artifact_path parameter to remove deprecation warnings. This improves MLflow tracking by: - Auto-generating model signature from training data - Using correct parameter names for MLflow 3.x - Enabling better model serving and inference validation 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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 DAGssrc/: Source codemodels/: Trained modelsdata/: Data filesnotebooks/: Jupyter notebookstests/: Test filesconfig/: Configuration filesdocker/: Docker fileskubernetes/: Kubernetes manifests
Description
Languages
Python
73.3%
Typst
25.9%
Dockerfile
0.8%