To monitor the project, we use the metrics & checks Dataiku feature.
For this, we defined two classes in the dataiku__metrics_checks.py module of the code. These classes are instanced in the metrics / checks tab of each dataset of folder that needs to be monitored and the right functions are called there. You will find more information on each of the class in their dedicated sub-pages.
You can also find the list of current checks used in the project here with their related element (dataset or folder) and error level (ERROR when we want a check failure to block the execution of the project, WARNING otherwise).
Below is an example of usage for a check :
From the "All_features_prices_df_joined" dataset, we can access the metrics and checks by using the "Status" tab.
The checks we use are using custom python probe to be able to use our code from the lib.
In the process() function, we can instantiate the MetricsAndChecks class and directly call any of the check developed inside. When calling a check, we also define the error level used and thresholds for the checks that use one.
Note : It is also possible to generate several checks from a single python probe by returning a dictionary if needed.