fix broken link

Author: msakande

articles/machine-learning/how-to-collect-production-data.md

@@ -207,7 +207,7 @@ def predict(input_df):
 
 #### Collect data for model performance monitoring
 
-If you want to use your collected data for model performance monitoring, it's important that each logged row has a unique `correlationid` that can be used to correlate the data with ground truth data, when such data becomes available. The data collector will autogenerate a unique `correlationid` for each logged row and include this autogenerated ID in the `correlationid` field in the JSON object. For more information on the JSON schema, see [store collected data in a blob](#store-collected-data-in-a-blob).
+If you want to use your collected data for model performance monitoring, it's important that each logged row has a unique `correlationid` that can be used to correlate the data with ground truth data, when such data becomes available. The data collector will autogenerate a unique `correlationid` for each logged row and include this autogenerated ID in the `correlationid` field in the JSON object. For more information on the JSON schema, see [store collected data in blob storage](#store-collected-data-in-blob-storage).
 
 If you want to use your own unique ID for logging with your production data, we recommend that you log this ID as a separate column in your pandas DataFrame, since the [data collector batches requests](#data-collector-batching) that are in close proximity to one another. By logging the `correlationid` as a separate column, it will be readily available downstream for integration with ground truth data.