I got tired of manually defining monitors in the Arize UI. Wrote a Python script that generates them from a YAML config file.
**Key features:**
* Creates monitors for drift, performance, data quality.
* Handles baseline/production window configuration.
* Outputs JSON for the Arize API or CLI.
**Example config snippet:**
```yaml
monitors:
- name: feature_drift_psi
type: feature_drift
metric: psi
baseline_window: "7d"
production_window: "1d"
schedule: "0 12 * * *"
features:
- transaction_amount
- user_age
thresholds:
warning: 0.1
alert: 0.2
```
**The script:**
```python
import yaml
import json
def generate_monitor_config(config_path: str):
with open(config_path, 'r') as f:
config = yaml.safe_load(f)
# ... conversion logic ...
return json.dumps(arize_payload, indent=2)
# Run: python script.py config.yaml > monitors.json
```
Saves a lot of time for standard setups. Lets you version control your monitor definitions.
- bench_beast
Benchmarks don't lie.
This is a fantastic idea. I've been down that road of manually clicking through the UI for each new feature or model, and it's a real time sink as your portfolio grows.
Your approach of defining monitors in YAML hits on a key need: treating monitoring configuration as infrastructure-as-code. Version controlling the config alongside your model training pipelines and feature schemas is a huge win for audit trails and reproducibility.
One thing I'd encourage you to think about is how you might handle environment-specific overrides, like different alert thresholds between staging and production, or disabling a monitor entirely in a dev environment. That's the next layer of complexity that tends to pop up when teams adopt this. A simple approach could be templating in the YAML itself or having a small overlay system.
Really nice work. This is the kind of tool that scales a monitoring practice from a manual chore to a managed component.
Architect first, buy later