diff --git a/template/config.yaml b/template/config.yaml
deleted file mode 100644
index bd4c34c..0000000
--- a/template/config.yaml
+++ /dev/null
@@ -1,78 +0,0 @@
-# {% include 'template/license_header' %}
-
-settings:
-  docker:
-    requirements:
-      - aws
-{%- if data_quality_checks %}
-      - evidently
-{%- endif %}
-      - kubeflow
-      - kubernetes
-      - mlflow
-      - sklearn
-      - slack
-extra:
-  mlflow_model_name: e2e_use_case_model
-{%- if target_environment == 'production' %}
-  target_env: Production
-{%- else %}
-  target_env: Staging
-{%- endif %}
-  notify_on_success: False
-  notify_on_failure: True
-{%- if hyperparameters_tuning %}
-  # This set contains all the models that you want to evaluate
-  # during hyperparameter tuning stage.
-  model_search_space:
-    random_forest:
-      model_package: sklearn.ensemble
-      model_class: RandomForestClassifier
-      search_grid:
-        criterion:
-          - gini
-          - entropy
-        max_depth:
-          - 2
-          - 4
-          - 6
-          - 8
-          - 10
-          - 12
-        min_samples_leaf:
-          range:
-            start: 1
-            end: 10
-        n_estimators:
-          range:
-            start: 50
-            end: 500
-            step: 25
-    decision_tree:
-      model_package: sklearn.tree
-      model_class: DecisionTreeClassifier
-      search_grid:
-        criterion:
-          - gini
-          - entropy
-        max_depth:
-          - 2
-          - 4
-          - 6
-          - 8
-          - 10
-          - 12
-        min_samples_leaf:
-          range:
-            start: 1
-            end: 10
-{%- else %}
-    # This model configuration will be used for the training stage.
-  model_configuration:
-    model_package: sklearn.tree
-    model_class: DecisionTreeClassifier
-    params:
-      criterion: gini
-      max_depth: 5
-      min_samples_leaf: 3
-{%- endif %}
\ No newline at end of file
diff --git a/template/main.py b/template/main.py
deleted file mode 100644
index d827559..0000000
--- a/template/main.py
+++ /dev/null
@@ -1,95 +0,0 @@
-# {% include 'template/license_header' %}
-
-from datetime import datetime as dt
-import os
-from typing import Optional
-
-from zenml.artifacts.external_artifact import ExternalArtifact
-from zenml.logger import get_logger
-
-from pipelines import {{product_name}}_batch_inference, {{product_name}}_training
-
-logger = get_logger(__name__)
-
-
-def main(
-    no_cache: bool = False,
-    no_drop_na: bool = False,
-    no_normalize: bool = False,
-    drop_columns: Optional[str] = None,
-    test_size: float = 0.2,
-    min_train_accuracy: float = 0.8,
-    min_test_accuracy: float = 0.8,
-    fail_on_accuracy_quality_gates: bool = False,
-    only_inference: bool = False,
-):
-    """Main entry point for the pipeline execution.
-
-    This entrypoint is where everything comes together:
-
-      * configuring pipeline with the required parameters
-        (some of which may come from command line arguments)
-      * launching the pipeline
-
-    Args:
-        no_cache: If `True` cache will be disabled.
-        no_drop_na: If `True` NA values will not be dropped from the dataset.
-        no_normalize: If `True` normalization will not be done for the dataset.
-        drop_columns: List of comma-separated names of columns to drop from the dataset.
-        test_size: Percentage of records from the training dataset to go into the test dataset.
-        min_train_accuracy: Minimum acceptable accuracy on the train set.
-        min_test_accuracy: Minimum acceptable accuracy on the test set.
-        fail_on_accuracy_quality_gates: If `True` and any of minimal accuracy
-            thresholds are violated - the pipeline will fail. If `False` thresholds will
-            not affect the pipeline.
-        only_inference: If `True` only inference pipeline will be triggered.
-    """
-
-    # Run a pipeline with the required parameters. This executes
-    # all steps in the pipeline in the correct order using the orchestrator
-    # stack component that is configured in your active ZenML stack.
-    pipeline_args = {
-        "config_path":os.path.join(
-            os.path.dirname(os.path.realpath(__file__)),
-            "config.yaml",
-            )
-        }
-    if no_cache:
-        pipeline_args["enable_cache"] = False
-
-    if not only_inference:
-        # Execute Training Pipeline
-        run_args_train = {
-            "drop_na": not no_drop_na,
-            "normalize": not no_normalize,
-            "random_seed": 42,
-            "test_size": test_size,
-            "min_train_accuracy": min_train_accuracy,
-            "min_test_accuracy": min_test_accuracy,
-            "fail_on_accuracy_quality_gates": fail_on_accuracy_quality_gates,
-        }
-        if drop_columns:
-            run_args_train["drop_columns"] = drop_columns.split(",")
-
-        pipeline_args[
-            "run_name"
-        ] = f"{{product_name}}_training_run_{dt.now().strftime('%Y_%m_%d_%H_%M_%S')}"
-        {{product_name}}_training.with_options(**pipeline_args)(**run_args_train)
-        logger.info("Training pipeline finished successfully!")
-
-    # Execute Batch Inference Pipeline
-    run_args_inference = {}
-    pipeline_args[
-        "run_name"
-    ] = f"{{product_name}}_batch_inference_run_{dt.now().strftime('%Y_%m_%d_%H_%M_%S')}"
-    {{product_name}}_batch_inference.with_options(**pipeline_args)(**run_args_inference)
-
-    artifact = ExternalArtifact(
-        pipeline_name="{{product_name}}_batch_inference",
-        artifact_name="predictions",
-    )
-    logger.info(
-        "Batch inference pipeline finished successfully! "
-        "You can find predictions in Artifact Store using ID: "
-        f"`{str(artifact.get_artifact_id())}`."
-    )
\ No newline at end of file
diff --git a/template/run.py b/template/run.py
index 77d6d92..123b830 100644
--- a/template/run.py
+++ b/template/run.py
@@ -140,12 +140,7 @@ def main(
     # Run a pipeline with the required parameters. This executes
     # all steps in the pipeline in the correct order using the orchestrator
     # stack component that is configured in your active ZenML stack.
-    pipeline_args = {
-        "config_path":os.path.join(
-            os.path.dirname(os.path.realpath(__file__)),
-            "config.yaml",
-            )
-        }
+    pipeline_args = {}
     if no_cache:
         pipeline_args["enable_cache"] = False