zenml-io · stefannica · Nov 25, 2024
diff --git a/examples/e2e/.copier-answers.yml b/examples/e2e/.copier-answers.yml
@@ -2,15 +2,13 @@
 _commit: 2024.10.30
 _src_path: gh:zenml-io/template-e2e-batch
 data_quality_checks: true
-email: [email protected]
-full_name: ZenML GmbH
-hyperparameters_tuning: true
+hyperparameters_tuning: false
 metric_compare_promotion: true
-notify_on_failures: true
+notify_on_failures: false
 notify_on_successes: false
-open_source_license: apache
-product_name: e2e_use_case
-project_name: ZenML E2E project
-target_environment: staging
+open_source_license: none
+product_name: supply_chain_forecasting
+project_name: Supply Chain Forecasting
+target_environment: production
 version: 0.0.1
 zenml_server_url: ''
diff --git a/examples/e2e/LICENSE b/examples/e2e/LICENSE
@@ -1,15 +0,0 @@
-Apache Software License 2.0
-
-Copyright (c) ZenML GmbH 2024. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.

diff --git a/examples/e2e/README.md b/examples/e2e/README.md
@@ -1,22 +1,22 @@
-# ZenML E2E project
+# Supply Chain Forecasting
 
 This is a comprehensive supervised ML project built with the
 ZenML framework and its integration. The project trains one or more
 scikit-learn classification models to make predictions on the tabular
 classification datasets provided by the scikit-learn library. The project was
 generated from the [E2E Batch ZenML project template](https://github.com/zenml-io/template-e2e-batch)
 with the following properties:
-- Project name: ZenML E2E project
-- Technical Name: e2e_use_case
+- Project name: Supply Chain Forecasting
+- Technical Name: supply_chain_forecasting
 - Version: `0.0.1`
-- Licensed with apache to ZenML GmbH<[email protected]>
-- Deployment environment: `staging`
+- Licensed with none to ZenML GmbH<>
+- Deployment environment: `production`
 
 Settings of your project are:
-- Hyperparameters and model architecture tuning using configuration from `config.py`
-- Trained model promotion to `staging` based on accuracy metric vs currently deployed model
+- Fixed model architecture defined in `config.py`
+- Trained model promotion to `production` based on accuracy metric vs currently deployed model
 - Data drift checks based on Evidently report
-- Notifications about failures enabled
+- All notifications disabled
 
 > [!NOTE]
 > If you are coming from [our production guide](https://docs.zenml.io/user-guide/production-guide), 
@@ -30,7 +30,7 @@ Settings of your project are:
 
 ## 👋 Introduction
 
-Welcome to your newly generated "ZenML E2E project" project! This is
+Welcome to your newly generated "Supply Chain Forecasting" project! This is
 a great way to get hands-on with ZenML using production-like template. 
 The project contains a collection of standard and custom ZenML steps, 
 pipelines and other artifacts and useful resources that can serve as a 
@@ -80,7 +80,7 @@ what you can do with ZenML.
 
 ## 📦 What's in the box?
 
-The ZenML E2E project project demonstrates how the most important steps of 
+The Supply Chain Forecasting project demonstrates how the most important steps of 
 the ML Production Lifecycle can be implemented in a reusable way remaining 
 agnostic to the underlying infrastructure, and how to integrate them together 
 into pipelines serving Training and Batch Inference purposes.

diff --git a/examples/e2e/configs/deployer_config.yaml b/examples/e2e/configs/deployer_config.yaml
@@ -1,18 +1,3 @@
-# Apache Software License 2.0
-# 
-# Copyright (c) ZenML GmbH 2024. All rights reserved.
-# 
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# 
-# http://www.apache.org/licenses/LICENSE-2.0
-# 
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
 # 
 
 # environment configuration
@@ -35,8 +20,8 @@ steps:
 
 # configuration of the Model Control Plane
 model:
-  name: e2e_use_case
-  version: staging
+  name: supply_chain_forecasting
+  version: production
 
 # pipeline level extra configurations
 extra:

diff --git a/examples/e2e/configs/inference_config.yaml b/examples/e2e/configs/inference_config.yaml
@@ -1,18 +1,3 @@
-# Apache Software License 2.0
-# 
-# Copyright (c) ZenML GmbH 2024. All rights reserved.
-# 
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# 
-# http://www.apache.org/licenses/LICENSE-2.0
-# 
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
 # 
 
 # environment configuration
@@ -35,8 +20,8 @@ steps:
 
 # configuration of the Model Control Plane
 model:
-  name: e2e_use_case
-  version: staging
+  name: supply_chain_forecasting
+  version: production
 
 # pipeline level extra configurations
 extra:

diff --git a/examples/e2e/configs/train_config.yaml b/examples/e2e/configs/train_config.yaml
@@ -1,18 +1,3 @@
-# Apache Software License 2.0
-# 
-# Copyright (c) ZenML GmbH 2024. All rights reserved.
-# 
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-# 
-# http://www.apache.org/licenses/LICENSE-2.0
-# 
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
 # 
 
 # environment configuration
@@ -31,80 +16,59 @@ settings:
 steps:
   model_trainer:
     parameters:
-      name: e2e_use_case
+      name: supply_chain_forecasting
   promote_with_metric_compare:
     parameters:
-      mlflow_model_name: e2e_use_case
+      mlflow_model_name: supply_chain_forecasting
   notify_on_success:
     parameters:
       notify_on_success: False
 
 # configuration of the Model Control Plane
 model:
-  name: e2e_use_case
-  license: apache
-  description: e2e_use_case E2E Batch Use Case
-  audience: All ZenML users
+  name: supply_chain_forecasting
+  license: Proprietary
+  description: |
+    Advanced forecasting model designed to optimize supply chain operations for jet engine manufacturing.
+    The model predicts demand, lead times, and inventory requirements for critical jet engine components
+    and raw materials, helping maintain optimal production schedules while minimizing inventory costs.
+  audience: |
+    - Supply Chain Managers
+    - Procurement Teams
+    - Production Planners
+    - Aerospace Manufacturing Operations
   use_cases: |
-    The ZenML E2E project project demonstrates how the most important steps of 
-    the ML Production Lifecycle can be implemented in a reusable way remaining 
-    agnostic to the underlying infrastructure, and shows how to integrate them together 
-    into pipelines for Training and Batch Inference purposes.
-  ethics: No impact.
+    - Predicts demand for critical jet engine components up to 12 months in advance
+    - Forecasts lead times for specialized materials and parts
+    - Optimizes inventory levels for high-value components
+    - Identifies potential supply chain bottlenecks
+    - Supports just-in-time manufacturing practices
+    - Helps reduce working capital tied up in inventory
+  limitations: |
+    - Requires minimum 3 years of historical data for optimal performance
+    - May need recalibration during major market disruptions
+    - Limited accuracy for newly introduced components
+  ethics: |
+    Low to Medium Risk:
+    - No personal data processing
+    - Critical for aviation safety - requires regular validation
+    - Environmental impact considerations in inventory optimization
   tags:
-  - e2e
-  - batch
-  - sklearn
-  - from template
-  - ZenML delivered
+  - supply_chain
+  - c2
+  - minimal risk
 
 # pipeline level extra configurations
 extra:
   notify_on_failure: True
 # pipeline level parameters
 parameters:
-  target_env: staging
-  # This set contains all the model configurations 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
+  target_env: production
+  # 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
diff --git a/examples/e2e/constants.py b/examples/e2e/constants.py
@@ -0,0 +1 @@
+DATA_CLASSIFICATION = "c2"
diff --git a/examples/e2e/pipelines/__init__.py b/examples/e2e/pipelines/__init__.py
@@ -1,21 +1,6 @@
-# Apache Software License 2.0
-#
-# Copyright (c) ZenML GmbH 2024. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
+# 
 
 
-from .batch_inference import e2e_use_case_batch_inference
-from .training import e2e_use_case_training
-from .deployment import e2e_use_case_deployment
+from .batch_inference import supply_chain_forecast_batch_inference
+from .deployment import supply_chain_forecast_deployment
+from .training import supply_chain_forecast_training
diff --git a/examples/e2e/pipelines/batch_inference.py b/examples/e2e/pipelines/batch_inference.py
@@ -1,20 +1,11 @@
-# Apache Software License 2.0
-#
-# Copyright (c) ZenML GmbH 2024. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
+# 
 
+from zenml import get_pipeline_context, pipeline
+from zenml.integrations.evidently.metrics import EvidentlyMetricConfig
+from zenml.integrations.evidently.steps import evidently_report_step
+from zenml.logger import get_logger
+
+from constants import DATA_CLASSIFICATION
 from steps import (
     data_loader,
     drift_quality_gate,
@@ -24,16 +15,11 @@
     notify_on_success,
 )
 
-from zenml import get_pipeline_context, pipeline
-from zenml.integrations.evidently.metrics import EvidentlyMetricConfig
-from zenml.integrations.evidently.steps import evidently_report_step
-from zenml.logger import get_logger
-
 logger = get_logger(__name__)
 
 
-@pipeline(on_failure=notify_on_failure)
-def e2e_use_case_batch_inference():
+@pipeline(on_failure=notify_on_failure, tags=[DATA_CLASSIFICATION])
+def supply_chain_forecast_batch_inference():
     """
     Model batch inference pipeline.
 
@@ -46,7 +32,8 @@ def e2e_use_case_batch_inference():
     model = get_pipeline_context().model
     ########## ETL stage  ##########
     df_inference, target, _ = data_loader(
-        random_state=model.get_artifact("random_state"), is_inference=True
+        random_state=model.get_artifact("random_state"),
+        is_inference=True
     )
     df_inference = inference_data_preprocessor(
         dataset_inf=df_inference,