fix older syntax

Author: wjayesh

databricks-production-qa-demo/steps/hp_tuning/hp_tuning_select_best_model.py

@@ -47,10 +47,10 @@ def hp_tuning_select_best_model(
     best_metric = -1
     # consume artifacts attached to current model version in Model Control Plane
     for step_name in step_names:
-        hp_output = model.get_data_artifact("hp_result")
+        hp_output = model.get_model_artifact("hp_result")
         model_: ClassifierMixin = hp_output.load()
         # fetch metadata we attached earlier
-        metric = float(hp_output.run_metadata["metric"].value)
+        metric = float(hp_output.run_metadata["metric"])
         if best_model is None or best_metric < metric:
             best_model = model_
     ### YOUR CODE ENDS HERE ###

databricks-production-qa-demo/steps/promotion/promote_with_metric_compare.py

@@ -89,14 +89,14 @@ def promote_with_metric_compare(
         # Promote in Model Registry
         latest_version_model_registry_number = latest_version.run_metadata[
             "model_registry_version"
-        ].value
+        ]
         if current_version_number is None:
             current_version_model_registry_number = (
                 latest_version_model_registry_number
             )
         else:
             current_version_model_registry_number = (
-                current_version.run_metadata["model_registry_version"].value
+                current_version.run_metadata["model_registry_version"]
             )
         promote_in_model_registry(
             latest_version=latest_version_model_registry_number,
@@ -108,7 +108,7 @@ def promote_with_metric_compare(
     else:
         promoted_version = current_version.run_metadata[
             "model_registry_version"
-        ].value
+        ]
 
     logger.info(
         f"Current model version in `{target_env}` is `{promoted_version}` registered in Model Registry"

databricks-production-qa-demo/steps/training/model_evaluator.py

@@ -2,7 +2,7 @@
 import mlflow
 import pandas as pd
 from sklearn.base import ClassifierMixin
-from zenml import step, get_step_context, log_model_metadata
+from zenml import step, get_step_context, log_metadata
 from zenml.client import Client
 from zenml.logger import get_logger
 from PIL import Image, ImageDraw, ImageFont, ImageFilter
@@ -34,13 +34,14 @@ def model_evaluator(
 
     step_context = get_step_context()
 
-    log_model_metadata(
+    log_metadata(
         metadata={
             "evaluation_metrics": {
                 "train_accuracy": trn_acc,
                 "test_accuracy": tst_acc
             }
         },
+        infer_model=True
     )
 
     # Fetch previous versions (same as before)
@@ -49,7 +50,7 @@ def model_evaluator(
     for version in client.get_model(step_context.model.name).versions:
         version_obj = client.get_model_version(step_context.model.name, version.version)
         if "evaluation_metrics" in version_obj.run_metadata:
-            test_accuracy = version_obj.run_metadata["evaluation_metrics"].value.get("test_accuracy")
+            test_accuracy = version_obj.run_metadata["evaluation_metrics"].get("test_accuracy")
             if test_accuracy is not None:
                 previous_versions.append((f"v{version.version}", float(test_accuracy)))