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Arize helps you visualize your model performance, understand drift & data quality issues, and share insights learned from your models. Spell is an end-to-end ML platform that provides infrastructure for company to deploy and train models.
You can either work through on Colab, or follow the steps below for your own model!
Step 1: Logging into spell via command line.
$ spell login
Step 2: Train and create model with spell.
$ spell run \
    --github-url https://github.com/spellml/examples \
    --machine-type cpu \
    --mount public/tutorial/churn_data/:/mnt/churn_prediction/ \
    --pip arize --pip lightgbm \
    -- python arize/train.py
Step 3: Add your Arize API_KEY and SPACE_ID to serve_async.py and server_sync.py. You can find your Arize credential details hereStep 4: Creating your model your model and serving it.
$ spell model create churn-prediction 'runs/$RUN_ID'
$ spell server serve \
    --node-group default \
    --min-pods 1 --max-pods 3 \
    --target-requests-per-second 100 \
    --pip lightgbm --pip arize \
    --env ARIZE_SPACE_ID=$ARIZE_SPACE_ID \
    --env ARIZE_API_KEY=$ARIZE_API_KEY \
    churn-prediction:v1 serve_sync.py  # or serve_async.py
Step 5: Test your working instance, send in some data, and see that your model is observable on Arize.
$ curl -X POST -d '@test_payload.txt' \
    https://$REGION.$CLUSTER.spell.services/$SPACE/churn-prediction/predict