How to: Experiment tracking
We'll use [MLflow](https://mlflow.org/) to track our experiments and store our models and the [MLflow Tracking UI](https://www.mlflow.org/docs/latest/tracking.html#tracking-ui) to view our experiments. We have been saving our experiments to a local directory but note that in an actual production setting, we would have a central location to store all of our experiments. It's easy/inexpensive to spin up your own MLflow server for all of your team members to track their experiments on or use a managed solution like [Weights & Biases](https://wandb.ai/site), [Comet](https://www.comet.ml/), etc.

```bash
export MODEL_REGISTRY=$(python -c "from madewithml import config; print(config.MODEL_REGISTRY)")
mlflow server -h 0.0.0.0 -p 8080 --backend-store-uri $MODEL_REGISTRY
```
Local
If you're running this notebook on your local laptop then head on over to
http://localhost:8080/
to view your MLflow dashboard.
Anyscale
If you're on
Anyscale Workspaces
, then we need to first expose the port of the MLflow server. Run the following command on your Anyscale Workspace terminal to generate the public URL to your MLflow server.

  ```bash
  APP_PORT=8080
  echo https://$APP_PORT-port-$ANYSCALE_SESSION_DOMAIN
  ```