Merge pull request #102791 from vmagelo/misc-fixes

Misc fixes

Author: PRMerger19

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

@@ -124,7 +124,7 @@ The [`AutoMLConfig`](https://docs.microsoft.com/python/api/azureml-train-automl-
 
 See the [reference documentation](/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig) for more information.
 
-Create the time-series settings as a dictionary object. Set the `time_column_name` to the `day_datetime` field in the data set. Define the `grain_column_names` parameter to ensure that **two separate time-series groups** are created for the data; one for store A and B. Lastly, set the `max_horizon` to 50 in order to predict for the entire test set. Set a forecast window to 10 periods with `target_rolling_window_size`, and specify a single lag on the target values for two periods ahead with the `target_lags` parameter. It is recommended to set `max_horizon`, `target_rolling_window_size` and `target_lags` to "auto" which will automatically detect these values for you. In the example below, "auto" settings have been used for these paramaters. 
+Create the time-series settings as a dictionary object. Set the `time_column_name` to the `day_datetime` field in the data set. Define the `grain_column_names` parameter to ensure that **two separate time-series groups** are created for the data; one for store A and B. Lastly, set the `max_horizon` to 50 in order to predict for the entire test set. Set a forecast window to 10 periods with `target_rolling_window_size`, and specify a single lag on the target values for two periods ahead with the `target_lags` parameter. It is recommended to set `max_horizon`, `target_rolling_window_size` and `target_lags` to "auto" which will automatically detect these values for you. In the example below, "auto" settings have been used for these parameters. 
 
 ```python
 time_series_settings = {
@@ -231,7 +231,7 @@ Calculate RMSE (root mean squared error) between the `actual_labels` actual valu
 from sklearn.metrics import mean_squared_error
 from math import sqrt
 
-rmse = sqrt(mean_squared_error(actual_lables, predict_labels))
+rmse = sqrt(mean_squared_error(actual_labels, predict_labels))
 rmse
 ```
 

articles/machine-learning/how-to-secure-web-service.md

@@ -78,7 +78,7 @@ To deploy (or redeploy) the service with SSL enabled, set the *ssl_enabled* para
 
 When you deploy to AKS, you can create a new AKS cluster or attach an existing one. For more information on creating or attaching a cluster, see [Deploy a model to an Azure Kubernetes Service cluster](how-to-deploy-azure-kubernetes-service.md).
 
--  If you create a new cluster, you use **[AksCompute.provisionining_configuration()](/python/api/azureml-core/azureml.core.compute.akscompute?view=azure-ml-py#provisioning-configuration-agent-count-none--vm-size-none--ssl-cname-none--ssl-cert-pem-file-none--ssl-key-pem-file-none--location-none--vnet-resourcegroup-name-none--vnet-name-none--subnet-name-none--service-cidr-none--dns-service-ip-none--docker-bridge-cidr-none--cluster-purpose-none--load-balancer-type-none--load-balancer-subnet-none-)**.
+-  If you create a new cluster, you use **[AksCompute.provisioning_configuration()](/python/api/azureml-core/azureml.core.compute.akscompute?view=azure-ml-py#provisioning-configuration-agent-count-none--vm-size-none--ssl-cname-none--ssl-cert-pem-file-none--ssl-key-pem-file-none--location-none--vnet-resourcegroup-name-none--vnet-name-none--subnet-name-none--service-cidr-none--dns-service-ip-none--docker-bridge-cidr-none--cluster-purpose-none--load-balancer-type-none--load-balancer-subnet-none-)**.
 - If you attach an existing cluster, you use **[AksCompute.attach_configuration()](/python/api/azureml-core/azureml.core.compute.akscompute?view=azure-ml-py#attach-configuration-resource-group-none--cluster-name-none--resource-id-none--cluster-purpose-none-)**. Both return a configuration object that has an **enable_ssl** method.
 
 The **enable_ssl** method can use a certificate that's provided by Microsoft or a certificate that you purchase.