[mlflow_utils] Mlflow upgrade 3.5

functions/src/mlflow_utils/function.yaml

@@ -1,32 +1,30 @@
+kind: serving
 verbose: false
+metadata:
+  categories:
+  - model-serving
+  - utilities
+  name: mlflow-utils
+  tag: ''
 spec:
-  command: ''
-  source: ''
-  default_class: MLFlowModelServer
+  image: mlrun/mlrun
   function_kind: serving_v2
+  disable_auto_mount: false
+  max_replicas: 4
+  min_replicas: 1
+  function_handler: mlflow-utils-nuclio:handler
   build:
     functionSourceCode: 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
     requirements:
-    - mlflow==2.12.2
-    - lightgbm
-    - xgboost
-    code_origin: ''
+    - mlflow~=3.5
     origin_filename: ''
-  image: mlrun/mlrun
+    code_origin: ''
+  description: Mlflow model server, and additional utils.
+  command: ''
   base_image_pull: false
+  default_class: MLFlowModelServer
+  source: ''
   default_handler: ''
-  max_replicas: 4
-  disable_auto_mount: false
-  min_replicas: 1
-  description: Mlflow model server, and additional utils.
-  function_handler: mlflow-utils-nuclio:handler
   env:
   - name: MLRUN_HTTPDB__NUCLIO__EXPLICIT_ACK
     value: enabled
-metadata:
-  categories:
-  - model-serving
-  - utilities
-  name: mlflow-utils
-  tag: ''
-kind: serving

functions/src/mlflow_utils/item.yaml

@@ -12,7 +12,7 @@ labels:
   author: Iguazio
 maintainers: []
 marketplaceType: ''
-mlrunVersion: 1.8.0
+mlrunVersion: 1.10.0
 name: mlflow_utils
 platformVersion: ''
 spec:
@@ -23,8 +23,6 @@ spec:
   image: mlrun/mlrun
   kind: serving
   requirements:
-  - mlflow~=2.22
-  - lightgbm
-  - xgboost
+  - mlflow~=3.5
 url: ''
-version: 1.1.0
+version: 1.2.0

functions/src/mlflow_utils/requirements.txt

@@ -1,3 +1,3 @@
-mlflow~=2.22
+mlflow~=3.5
 lightgbm
 xgboost

functions/src/mlflow_utils/test_mlflow_utils.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 #
 import tempfile
-
+import shutil
 import lightgbm as lgb
 import mlflow
 import mlflow.environment_variables
@@ -132,7 +132,9 @@ def test_track_run_with_experiment_name(handler):
     # Set the mlflow experiment name
     mlflow.environment_variables.MLFLOW_EXPERIMENT_NAME.set(f"{handler}_test_track")
     with tempfile.TemporaryDirectory() as test_directory:
-        mlflow.set_tracking_uri(test_directory)  # Tell mlflow where to save logged data
+        # Use SQLite backend instead of filesystem (filesystem will be deprecated in Feb 2026)
+        db_uri = f"sqlite:///{os.path.join(test_directory, 'mlflow.db')}"
+        mlflow.set_tracking_uri(db_uri)  # Tell mlflow where to save logged data
 
         # Create a project for this tester:
         project = mlrun.get_or_create_project(name="default", context=test_directory)
@@ -149,17 +151,43 @@ def test_track_run_with_experiment_name(handler):
         trainer_run = func.run(
             local=True,
             handler=handler,
-            artifact_path=test_directory,
+            output_path=test_directory,
         )
 
+        # Find the MLflow logged model and prepare it for serving
+        # Note: In MLflow 2.24+, we must dynamically discover model paths since MLflow changed
+        # its directory structure from predictable paths (e.g., experiment_name/0/model/) to
+        # UUID-based paths (e.g., experiment_id/run_uuid/artifacts/model/).
+
+        # Create MLflow client to query the tracking server
+        mlflow_client = mlflow.tracking.MlflowClient(tracking_uri=db_uri)
+
+        # Get the experiment by name to obtain its ID
+        experiment = mlflow_client.get_experiment_by_name(f"{handler}_test_track")
+
+        # Search for runs in this experiment and get the run ID
+        # (There should only be one run from our training above)
+        run_id = mlflow_client.search_runs(experiment_ids=[experiment.experiment_id])[0].info.run_id
+
+        # Find all models logged in this run
+        logged_models = mlflow.search_logged_models(filter_string=f"source_run_id = '{run_id}'")
+
+        # Extract the artifact location and remove the "file://" prefix
+        model_artifacts_dir = logged_models["artifact_location"].tolist()[0].replace("file://", "")
+
+        # Package the model artifacts as a zip file for MLFlowModelServer
+        # Note: MLFlowModelServer requires models to be packaged as zip archives
+        # rather than loose directories for deployment
+        model_path = os.path.join(test_directory, f"{handler}-model-serving")
+        os.makedirs(model_path, exist_ok=True)
+        shutil.make_archive(os.path.join(model_path, "model"), 'zip', model_artifacts_dir)
+
         serving_func = project.set_function(
             func=os.path.abspath("function.yaml"),
             name=f"{handler}-server",
         )
         model_name = f"{handler}-model"
         # Add the model
-        upper_handler = handler.replace("_", "-")
-        model_path = test_directory + f"/{upper_handler}-test-{upper_handler}/0/model/"
         serving_func.add_model(
             model_name,
             class_name="MLFlowModelServer",