Ranking

Ranking models are often used in recommendation systems, ads, search engines, etc. In Aporia, these models are represented with the ranking model type.

Integration

If you have a ranking or recommendations model, then your database may look like the following:

id	feature1 (numeric)	feature2 (boolean)	scores (array)	relevance (array)	timestamp (datetime)
1	13.5	True	[9, 8, 10, ...]	[2, 0, 1, ...]	2014-10-19 10:23:54
2	-8	False	[4.5, 8.7, 9, ...]	[0, 1, 2, ...]	2014-10-19 10:24:24

To monitor a ranking model, create a new model version with an array field(s):

apr_model = aporia.create_model_version(
  model_id="<MODEL_ID>", # You will need to create a model with this MODEL_ID in advance
  model_version="v1",
  model_type="ranking"
  features={
     ...
  },
  predictions={
    "scores": "array"
  },
)

To connect your data source to this model in Aporia, please call the connect_serving(...) API:

apr_model.connect_serving(
  data_source=my_data_source,

  id_column="id",
  timestamp_column="timestamp",

  predictions={
    # Prediction name -> Column name representing 
    "relevance": "scores"
  }
)

Check out the Data Sources section for further reading on the available data sources and how to connect to each one of them.