- Add Prometheus, Grafana, and Loki monitoring stack - Include pre-configured dashboards for ML metrics and logs - Add Podman container support with security policies - Implement ML runtime environments for multiple frameworks - Add containerized ML project templates (PyTorch, TensorFlow, etc.) - Include secure runner with isolation and resource limits - Add comprehensive log aggregation and alerting |
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| .. | ||
| results | ||
| README.md | ||
| requirements.txt | ||
| train.py | ||
Scikit-learn Experiment
Random Forest classification project using scikit-learn.
Usage
python train.py --n_estimators 100 --output_dir ./results
Results
Results are saved in JSON format with accuracy and model metrics.