fix registry references and add preview tag

Author: EricWrightAtWork

articles/machine-learning/how-to-auto-train-forecast.md

@@ -757,6 +757,8 @@ Once the job is submitted, AutoML will provision compute resources, apply featur
 
 ## Orchestrating training, inference, and evaluation with components and pipelines
 
+[!INCLUDE [preview v2](includes/machine-learning-preview-generic-disclaimer.md)]
+
 Your ML workflow likely requires more than just training. Inference, or retrieving model predictions on newer data, and evaluation of model accuracy on a test set with known target values are other common tasks that you can orchestrate in AzureML along with training jobs. To support inference and evaluation tasks, AzureML provides [components](concept-component.md), which are self-contained pieces of code that do one step in an AzureML [pipeline](concept-ml-pipelines.md).
 
 # [Python SDK](#tab/python)
@@ -776,11 +778,16 @@ except Exception as ex:
     # Fall back to InteractiveBrowserCredential in case DefaultAzureCredential fails
     credential = InteractiveBrowserCredential()
 
-# Create a client for accessing assets in the AzureML registry
+# Create a client for accessing assets in the AzureML preview registry
 ml_client_registry = MLClient(
     credential=credential,
-    registry_name="<REGISTRY_NAME>",
-    registry_location="<REGISTRY_REGION>"
+    registry_name="azureml-preview"
+)
+
+# Create a client for accessing assets in the AzureML preview registry
+ml_client_metrics_registry = MLClient(
+    credential=credential,
+    registry_name="azureml"
 )
 
 # Get an inference component from the registry
@@ -790,7 +797,7 @@ inference_component = ml_client_registry.components.get(
 )
 
 # Get a component for computing evaluation metrics from the registry
-compute_metrics_component = ml_client_registry.components.get(
+compute_metrics_component = ml_client_metrics_registry.components.get(
     name="compute_metrics",
     label="latest"
 )
@@ -961,7 +968,7 @@ jobs:
     # Configure the inference node to make rolling forecasts on the test set
     inference_node:
         type: command
-        component: azureml://registries/ForecastingDemand2/components/automl_forecasting_inference/versions/0.0.1.2.preview
+        component: azureml://registries/azureml-preview/components/automl_forecasting_inference@latest
         inputs:
             target_column_name: ${{parent.inputs.target_column_name}}
             forecast_mode: rolling
@@ -976,7 +983,7 @@ jobs:
     # Configure the metrics calculation node
     compute_metrics:
         type: command
-        component: azureml://registries/ForecastingDemand2/components/compute_metrics/versions/0.0.14.preview
+        component: azureml://registries/azureml/compute_metrics@latest
         inputs:
             task: "tabular-forecasting"
             ground_truth: ${{parent.jobs.inference_node.outputs.inference_output_file}}
@@ -1023,6 +1030,8 @@ For more details on rolling evaluation, see our [forecasting model evaluation ar
 
 ## Forecasting at scale: many models
 
+[!INCLUDE [preview v2](includes/machine-learning-preview-generic-disclaimer.md)]
+
 The many models components in AutoML enable you to train and manage millions of models in parallel. For more information on many models concepts, see the [many models article section](concept-automl-forecasting-at-scale.md#many-models).
 
 
@@ -1100,18 +1109,18 @@ except Exception as ex:
 
 # Get a many models training component
 mm_train_component = ml_client_registry.components.get(
-    name='automl_many_model_training',
+    name='automl_many_models_training',
     version='latest'
 )
 
 # Get a many models inference component
 mm_inference_component = ml_client_registry.components.get(
-    name='automl_many_model_inferencing',
+    name='automl_many_models_inference',
     version='latest'
 )
 
 # Get a component for computing evaluation metrics
-compute_metrics_component = ml_client_registry.components.get(
+compute_metrics_component = ml_client_metrics_registry.components.get(
     name="compute_metrics",
     label="latest"
 )
@@ -1232,7 +1241,7 @@ jobs:
     # Configure AutoML many models training component
     mm_train_node:
         type: command
-        component: azureml://registries/ManyModels_HTS_BugBash/components/automl_many_model_training/versions/0.1.33
+        component: azureml://registries/azureml-preview/components/automl_many_models_training@latest
         inputs:
             raw_data: ${{parent.inputs.train_data_input}}
             automl_config: ${{parent.inputs.automl_config_input}}
@@ -1248,7 +1257,7 @@ jobs:
     # Configure the inference node to make rolling forecasts on the test set
     mm_inference_node:
         type: command
-        component: azureml://registries/ManyModels_HTS_BugBash/components/automl_many_model_inferencing/versions/0.1.33
+        component: azureml://registries/azureml-preview/components/automl_many_models_inference@latest
         inputs:
             raw_data: ${{parent.inputs.test_data_input}}
             max_concurrency_per_instance: ${{parent.inputs.max_concurrency_per_instance}}
@@ -1268,7 +1277,7 @@ jobs:
     # Configure the metrics calculation node
     compute_metrics:
         type: command
-        component: azureml://registries/ForecastingDemand2/components/compute_metrics/versions/0.0.14.preview
+        component: azureml://registries/azureml/components/compute_metrics@latest
         inputs:
             task: "tabular-forecasting"
             ground_truth: ${{parent.jobs.mm_inference_node.outputs.evaluation_data}}
@@ -1293,6 +1302,8 @@ After the job finishes, the evaluation metrics can be downloaded locally using t
 
 ## Forecasting at scale: hierarchical time series
 
+[!INCLUDE [preview v2](includes/machine-learning-preview-generic-disclaimer.md)]
+
 The hierarchical time series (HTS) components in AutoML enable you to train a large number of models on data with hierarchical structure. For more information, see the [HTS article section](concept-automl-forecasting-at-scale.md#hierarchical-time-series-forecasting).
 
 ### HTS training configuration
@@ -1376,12 +1387,12 @@ hts_train_component = ml_client_registry.components.get(
 
 # Get a HTS inference component
 hts_inference_component = ml_client_registry.components.get(
-    name='automl_hts_inferencing',
+    name='automl_hts_inference',
     version='latest'
 )
 
 # Get a component for computing evaluation metrics
-compute_metrics_component = ml_client_registry.components.get(
+compute_metrics_component = ml_client_metrics_registry.components.get(
     name="compute_metrics",
     label="latest"
 )
@@ -1499,7 +1510,7 @@ jobs:
     # Configure AutoML many models training component
     hts_train_node:
         type: command
-        component: azureml://registries/ManyModels_HTS_BugBash/components/automl_hts_training/versions/0.1.33
+        component: azureml://registries/azureml-preview/components/automl_hts_training@latest
         inputs:
             raw_data: ${{parent.inputs.train_data_input}}
             automl_config: ${{parent.inputs.automl_config_input}}
@@ -1514,7 +1525,7 @@ jobs:
     # Configure the inference node to make rolling forecasts on the test set
     hts_inference_node:
         type: command
-        component: azureml://registries/ManyModels_HTS_BugBash/components/automl_hts_inferencing/versions/0.1.33
+        component: azureml://registries/azureml-preview/components/automl_hts_inference@latest
         inputs:
             raw_data: ${{parent.inputs.test_data_input}}
             max_concurrency_per_instance: ${{parent.inputs.max_concurrency_per_instance}}
@@ -1536,7 +1547,7 @@ jobs:
     # Configure the metrics calculation node
     compute_metrics:
         type: command
-        component: azureml://registries/ForecastingDemand2/components/compute_metrics/versions/0.0.14.preview
+        component: azureml://registries/azureml/components/compute_metrics@latest
         inputs:
             task: "tabular-forecasting"
             ground_truth: ${{parent.jobs.hts_inference_node.outputs.evaluation_data}}