few more renames

README.md

@@ -308,7 +308,7 @@ You can follow [Data Validators docs](https://docs.zenml.io/stacks-and-component
 
 As a last step concluding all work done so far, we will calculate predictions on the inference dataset and persist them in [Artifact Store](https://docs.zenml.io/stacks-and-components/component-guide/artifact-stores) attached to the current inference model version of the Model Control Plane for reuse and observability.
 
-We will leverage a prepared predictions service called `mlflow_deployment` linked to the inference model version of the Model Control Plane to run `.predict()` and to put predictions as an output of the predictions step, so it is automatically stored in the [Artifact Store](https://docs.zenml.io/stacks-and-components/component-guide/artifact-stores) and linked to the Model Control Plane model version as a versioned artifact link with zero effort. This is achieved because we additionally annotated the `predictions` output with `ArtifactConfig(overwrite=False)`. This is required to deliver a comprehensive history to stakeholders since Batch Inference can be executed using the same Model Control Plane version multiple times.
+We will leverage a prepared predictions service called `mlflow_deployment` linked to the inference model version of the Model Control Plane to run `.predict()` and to put predictions as an output of the predictions step, so it is automatically stored in the [Artifact Store](https://docs.zenml.io/stacks-and-components/component-guide/artifact-stores) and linked to the Model Control Plane model version as a versioned artifact link with zero effort. This is achieved because we additionally annotated the `predictions` output with `DataArtifactConfig(overwrite=False)`. This is required to deliver a comprehensive history to stakeholders since Batch Inference can be executed using the same Model Control Plane version multiple times.
 
 ```
 NOTE: On non-local orchestrators a `model` artifact will be loaded into memory to run predictions directly. You can adapt this part to your needs.
@@ -318,12 +318,12 @@ NOTE: On non-local orchestrators a `model` artifact will be loaded into memory t
   <summary>Code snippet 💻</summary>
 
 ```python
-from zenml.model import ArtifactConfig
+from zenml.model import DataArtifactConfig
 
 @step
 def inference_predict(
     dataset_inf: pd.DataFrame,
-) -> Annotated[pd.Series, "predictions", ArtifactConfig(overwrite=False)]:
+) -> Annotated[pd.Series, "predictions", DataArtifactConfig(overwrite=False)]:
     model_version = get_step_context().model_version
 
     # get predictor

template/steps/deployment/deployment_deploy.py

@@ -11,7 +11,7 @@
     mlflow_model_registry_deployer_step,
 )
 from zenml.logger import get_logger
-from zenml.model import DeploymentArtifactConfig
+from zenml.model import EndpointArtifactConfig
 
 logger = get_logger(__name__)
 
@@ -21,7 +21,7 @@ def deployment_deploy() -> (
     Annotated[
         Optional[MLFlowDeploymentService],
         "mlflow_deployment",
-        DeploymentArtifactConfig(),
+        EndpointArtifactConfig(),
     ]
 ):
     """Predictions step.

template/steps/etl/inference_data_preprocessor.py

@@ -6,7 +6,7 @@
 import pandas as pd
 from sklearn.pipeline import Pipeline
 from zenml import step
-from zenml.model import ArtifactConfig
+from zenml.model import DataArtifactConfig
 
 
 @step
@@ -17,7 +17,7 @@ def inference_data_preprocessor(
 ) -> Annotated[
     pd.DataFrame,
     "dataset_inf",
-    ArtifactConfig(overwrite=False, artifact_name="inference_dataset"),
+    DataArtifactConfig(overwrite=False, artifact_name="inference_dataset"),
 ]:
     """Data preprocessor step.
 

template/steps/inference/inference_predict.py

@@ -8,15 +8,15 @@
 from zenml import get_step_context, step
 from zenml.integrations.mlflow.services.mlflow_deployment import MLFlowDeploymentService
 from zenml.logger import get_logger
-from zenml.model import ArtifactConfig
+from zenml.model import DataArtifactConfig
 
 logger = get_logger(__name__)
 
 
 @step
 def inference_predict(
     dataset_inf: pd.DataFrame,
-) -> Annotated[pd.Series, "predictions", ArtifactConfig(overwrite=False)]:
+) -> Annotated[pd.Series, "predictions", DataArtifactConfig(overwrite=False)]:
     """Predictions step.
 
     This is an example of a predictions step that takes the data in and returns