%md This notebook should only be run in a Databricks Job, as part of MLflow 3.0 Deployment Jobs.
This notebook should only be run in a Databricks Job, as part of MLflow 3.0 Deployment Jobs.
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%pip install mlflow --upgrade --pre dbutils.library.restartPython()
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dbutils.widgets.text("model_name", "") dbutils.widgets.text("model_version", "")
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import pandas as pd from sklearn.datasets import load_iris def sample_iris_data(): iris = load_iris() iris_df = pd.DataFrame(data=iris.data, columns=iris.feature_names) iris_df['quality'] = (iris.target == 2).astype(int) return iris_df
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import mlflow # TODO: add evaluation dataset and target here data = sample_iris_data() target = "quality" # TODO: add model type here (e.g. "regressor", "databricks-agent", etc.) model_type = "regressor" model_name = dbutils.widgets.get("model_name") model_version = dbutils.widgets.get("model_version") model_uri = "models:/" + model_name + "/" + model_version # can also fetch model ID and use that for URI instead as described below with mlflow.start_run(run_name="evaluation") as run: mlflow.evaluate( model=model_uri, data=data, targets=target, model_type=model_type )