Where you want your predictions to go

Now that you've declared the predictions you want to deploy with a scope, you'll add one or more Targets to indicate where they should be deployed.


Faraday continuously renders each scope in the system to produce a spreadsheet-like table where each payload element gets a column and each member of the population gets a row. When you add a target, you're telling Faraday to copy the "spreadsheet" to a place where you can retrieve the predictions you need to support your business.

Target representations

A target represents its underlying pipeline and can do so in several ways, controlled by the representation parameter on the POST /targets call.


Example use case: deploying audiences to ad platforms.

Hashes all identity data (e.g. email, address) in the output. This option may produce multiple rows per person — one for each hashed identity Faraday associates with that person — including people the account has not previously identified in a dataset.


Precise outcome (propensity) score columns will be excluded from the hashed target output to protect privacy.


Example use case: deploying to a CDP, CRM, database, or email platform to enhance known contacts with predictions.

This option will produce one row per person, limited to people already identified in a dataset of your choice. To protect privacy, this will not include identifying information other than the reference key (e.g. customer ID or email address) you select, which must be done at the dataset level first.


Example use case: direct mail or canvassing campaign.

This option will produce one row per person or household and, depending on your plan, may include people not previously identified in a dataset in your account.


The number of people unknown to your account (i.e., not included in any dataset) included in the output, if any, is governed by your plan and will be limited to a fixed cap.


Example use case: geotargeted ad campaigns

This option will produce one row per geographical area (e.g. state or zipcode) with aggregated payload elements.


To avoid confusion, traits in your payload will not be included in aggregated targets.

Available targets

Faraday natively supports many deployment targets. They organize into two categories:

  • Publication targets — Faraday hosts your predictions for convenient retrieval as needed.
  • Replication targets — Faraday copies your predictions to systems you control.

Publication targets

Often the easiest to start with, publication targets are securely hosted by Faraday, so they don't require you to establish connections to your systems up front.

Currently supported:

  • CSV — Faraday hosts a CSV for you to retrieve by your choice of protocol: HTTPS, S3, GCS, or SFTP.
  • API — Faraday hosts your predictions in a high-speed JSON key-value store for you to retrieve individually in real time using an HTTP API.

Coming soon:

  • SQL — Faraday hosts your predictions in a SQL-compatible database table for you to query as needed.

Replication targets

Faraday can also copy your predictions to a system you control. You'll first need to establish a Connection to that system, then you can add the corresponding target to your scope.

Under the hood, Faraday uses its open-source dbcrossbar tool to replicate your data from the Faraday platform to your systems. Currently, dbcrossbar supports the following:

AWS S3CSV file
Google Cloud Storage (GCS)CSV file

Adding a target

You'll use POST /targets queries to add targets. Here's a sample request:

curl --request POST \
     --url \
     --header 'Authorization: Bearer YOUR_API_KEY' \
     --header 'Content-Type: application/json' \
     --data '
     "name": "TARGET_NAME",
     "options": {
          "type": "hosted_csv"
     "representation": {
          "mode": "hashed"
     "scope_id": "YOUR_SCOPE_ID"

Adding publication targets only requires a type parameter; replication targets require an existing Connection and additional parameters. See the docs on individual targets for more information.


You're done!

That's all there is to making and deploying predictions with Faraday. If you haven't already, check out one of the quickstart tutorials listed below. Also, feel free to join our Discord anytime to share ideas and get help.