Generating segments based on propensity scores configured via a templated model builder experience

1 . A method for data processing, comprising:

receiving, at a user interface associated with a data service that manages a data model of a plurality of entities, a first user input indicating a selection of a first set of entities of the plurality of entities for training an artificial intelligence model, wherein the first set of entities corresponds to one or more users;

receiving, at the user interface, a second user input indicating a selection of a set of outcome conditions associated with the first set of entities, the second user input being different from the first user input, wherein the set of outcome conditions define an output of the artificial intelligence model based at least in part on a set of input data associated with the first set of entities;

generating the artificial intelligence model based at least in part on the set of outcome conditions and the set of input data, wherein the artificial intelligence model is generated to indicate that the set of input data satisfies the set of outcome conditions;

generating, based at least in part on execution of the artificial intelligence model, a set of prediction metrics for a second set of entities, the set of prediction metrics corresponding to the set of outcome conditions;

storing an indication of the generated set of prediction metrics in association with the second set of entities in the data model; and

generating, using the data service and based at least in part on the indication of the generated set of prediction metrics, a segment of entities of the second set of entities.

2 . The method of claim 1 , further comprising:

generating, based at least in part on executing the artificial intelligence model, a distribution of entities of the second set of entities relative to a plurality of thresholds corresponding to the generated set of prediction metrics.

3 . The method of claim 2 , further comprising:

generating the set of prediction metrics in accordance with the distribution of the segment of entities.

4 . The method of claim 2 , wherein the plurality of thresholds comprises a least likely threshold, a less likely threshold, a more likely threshold, a most likely threshold, or any combination thereof.

5 . The method of claim 2 , further comprising:

receiving the second user input indicating the plurality of thresholds.

6 . The method of claim 1 , further comprising:

displaying, at the user interface, a model progress indicator comprising a summary of the generated artificial intelligence model, wherein the summary indicates the first user input and the second user input.

7 . The method of claim 1 , further comprising:

generating, at the user interface, the set of outcome conditions associated with the first set of entities; and

receiving, at the user interface, the second user input indicating the selection of the set of outcome conditions based at least in part on generating the set of outcome conditions.

8 . The method of claim 1 , wherein receiving the first user input comprises:

receiving the first user input indicating the set of input data, wherein the set of input data comprises a set of attributes associated with the first set of entities; and

training the artificial intelligence model using the first set of entities and the set of attributes.

9 . An apparatus for data processing, comprising:

a processor;

memory coupled with the processor; and

instructions stored in the memory and executable by the processor to cause the apparatus to:

receive, at a user interface associated with a data service that manages a data model of a plurality of entities, a first user input indicating a selection of a first set of entities of the plurality of entities for training an artificial intelligence model, wherein the first set of entities corresponds to one or more users;

receive, at the user interface, a second user input indicating a selection of a set of outcome conditions associated with the first set of entities, wherein the second user input is different from the first user input, and wherein the set of outcome conditions define an output of the artificial intelligence model based at least in part on a set of input data associated with the first set of entities;

generate the artificial intelligence model based at least in part on the set of outcome conditions and the set of input data, wherein the artificial intelligence model is generated to indicate that the set of input data satisfies the set of outcome conditions;

generate, based at least in part on execution of the artificial intelligence model, a set of prediction metrics for a second set of entities, the set of prediction metrics corresponding to the set of outcome conditions;

store an indication of the generated set of prediction metrics in association with the second set of entities in the data model; and

generate, using the data service and based at least in part on the indication of the generated set of prediction metrics, a segment of entities of the second set of entities.

10 . The apparatus of claim 9 , wherein the instructions are further executable by the processor to cause the apparatus to:

generate, based at least in part on executing the artificial intelligence model, a distribution of entities of the second set of entities relative to a plurality of thresholds corresponding to the generated set of prediction metrics.

11 . The apparatus of claim 10 , wherein the instructions are further executable by the processor to cause the apparatus to:

generate the set of prediction metrics in accordance with the distribution of the segment of entities.

12 . The apparatus of claim 10 , wherein the plurality of thresholds comprises a least likely threshold, a less likely threshold, a more likely threshold, a most likely threshold, or any combination thereof.

13 . The apparatus of claim 10 , wherein the instructions are further executable by the processor to cause the apparatus to:

receive the second user input indicating the plurality of thresholds.

14 . The apparatus of claim 9 , wherein the instructions are further executable by the processor to cause the apparatus to:

display, at the user interface, a model progress indicator comprising a summary of the generated artificial intelligence model, wherein the summary indicates the first user input and the second user input.

15 . The apparatus of claim 9 , wherein the instructions are further executable by the processor to cause the apparatus to:

generate, at the user interface, the set of outcome conditions associated with the first set of entities; and

receive, at the user interface, the second user input indicating the selection of the set of outcome conditions based at least in part on generating the set of outcome conditions.

16 . The apparatus of claim 9 , wherein the instructions to receive the first user input are executable by the processor to cause the apparatus to:

receive the first user input indicating the set of input data, wherein the set of input data comprises a set of attributes associated with the first set of entities; and

train the artificial intelligence model using the first set of entities and the set of attributes.

17 . A non-transitory computer-readable medium storing code for data processing, the code comprising instructions executable by a processor to:

receive, at a user interface associated with a data service that manages a data model of a plurality of entities, a first user input indicating a selection of a first set of entities of the plurality of entities for training an artificial intelligence model, wherein the first set of entities corresponds to one or more users;

receive, at the user interface, a second user input indicating a selection of a set of outcome conditions associated with the first set of entities, the second user input being different from the first user input, wherein the set of outcome conditions define an output of the artificial intelligence model based at least in part on a set of input data associated with the first set of entities;

generate the artificial intelligence model based at least in part on the set of outcome conditions and the set of input data, wherein the artificial intelligence model is generated to indicate that the set of input data satisfies the set of outcome conditions;

generate, based at least in part on execution of the artificial intelligence model, a set of prediction metrics for a second set of entities, the set of prediction metrics corresponding to the set of outcome conditions;

store an indication of the generated set of prediction metrics in association with the second set of entities in the data model; and

generate, used the data service and based at least in part on the indication of the generated set of prediction metrics, a segment of entities of the second set of entities.

18 . The non-transitory computer-readable medium of claim 17 , wherein the instructions are further executable by the processor to:

generate, based at least in part on executing the artificial intelligence model, a distribution of entities of the second set of entities relative to a plurality of thresholds corresponding to the generated set of prediction metrics.

19 . The non-transitory computer-readable medium of claim 18 , wherein the instructions are further executable by the processor to:

generate the set of prediction metrics in accordance with the distribution of the segment of entities.

20 . The non-transitory computer-readable medium of claim 18 , wherein the plurality of thresholds comprises a least likely threshold, a less likely threshold, a more likely threshold, a most likely threshold, or any combination thereof.