Delayed Actuals

Connect model predictions to delayed ground truth data

What Are Delayed (Latent) Actuals

Depending on your model use case, you may experience a delayed feedback loop when collecting ground truth data. We call this data delayed actuals.

If your model receives delayed actuals, Arize can automatically connect actuals to predictions sent earlier via the same prediction ID.

Sending Delayed Actuals

Utilize the Arize joiner to easily match delayed actuals with predictions in the Arize platform. To do this, simply upload your actuals data using the same prediction_id as its corresponding prediction.

Joiner Cadence & Lookback

The Arize joiner automatically triggers daily at 05:00 UTC to map delayed actuals with their corresponding prediction values up to 14 days from when the prediction was received. This is supported for all data upload methods.

Joins are conducted on actuals sent within the join window for the day prior, which is from 00:00 UTC to 23:59 UTC.

The Arize support team can extend your 14-day connection window and increase your joiner cadence upon request. Reach out to support@arize.com for help.

Joiner Requirements

FieldDescription

prediction_id

(required) A prediction's unique identifier. The actual's prediction_id must match its corresponding prediction to join the data

actual_score / actual_label For ranking models only: relevance_label

(required) The ground truth values of your model. The use of score and label varies based on model type

model_id

(required) When sending delayed actuals, specify the model_id in your schema to match your actuals to the correct model

Upload delayed actuals for ranking models with file/table upload via GraphQL or SDK. Native UI upload support coming soon. Reach out to support@arize.com for help and questions.

Example Joins By Upload Method

To send delayed actuals via GCS, AWS S3, Azure Blob Storage, Google BigQuery, and Snowflake, configure separate data ingestion jobs for predictions and actuals. We recommend naming job prefixes to indicate which job contains predictions or actuals.

gs://bucket1/click-thru-rate/prediction/
├── 11-19-2022.parquet 
├── 11-20-2022.parquet
├── 11-21-2022.parquet
gs://bucket1/click-thru-rate/actuals/
├── 12-1-2022.parquet # same prediction id column, model, and space as the corresponding prediction
├── 12-2-2022.parquet
└── 12-3-2022.parquet

Make sure that your prediction ID, model name, and space match with your corresponding predictions when defining the schema for these two data ingestion jobs. Once you configure both jobs, Arize will automatically recognize and sync new prediction and actual data. To validate new data in Arize, visualize the data in the 'Dataset' tab.

Tags with Delayed Actuals

Tags can be updated via delayed actuals. If tags are sent with actuals, the tag will be joined based on prediction_id. However, if the actual was sent prior, then resent with an updated tag value, the tag value will not be updated.

Tags will remain the same if they are sent with predictions, not actuals.

For example, if a user sends Arize a prediction with tags:

"location": "New York"
"month": "January"

And actual with tags:

"location": "Chicago"
"fruit": "apple"

The resulting tags available will be:

"location": "New York"
"month": "January"
"fruit": "apple"

Measure Model Performance

Arize only calculates performance metrics on predictions that have actuals, so once your join is represented in Arize, you can utilize performance metrics and the 'Performance Tracing' tab for those predictions.

If actuals have not been received yet (delayed actuals), use drift as a proxy metric for model performance to measure and monitor model health.

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