---
sourceDocument: Yokohama Enable AI
sourceDocumentLink: https://servicenow-prod.fluidtopics.net/r/yokohama/intelligent-experiences

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    - yokohama

ft:locale :

    - en-US

ft:publication_title :

    - Yokohama Enable AI

ft:clusterId :

    - platai

bundleId :

    - platai

workflow :

    - Platform


---

# Creating and training solutions

# Creating and training solutions {#ariaid-title1}

Release version: Yokohama  
Updated January 30, 2025  
![](https://www.servicenow.com/docs/portal-asset/ico-clock) 2 minutes to read
Summarize  
![AI sparkle icon](https://servicenow.com/docs/portal-asset/ai-sparkle-icon) Summarized using AI  
This content was generated using new OpenAI-powered functionality. Results are provided on an as is basis and are not guaranteed to be accurate or complete.  

## Summary of Creating and training solutions

ServiceNow's Predictive Intelligence (PI) frameworks enable you to create and train machine-learning solutions that predict, recommend, and organize data outcomes.
These solutions can be accessed by any application via a prediction API, helping automate categorization, recommendation, and pattern identification based on historical data.
Show full answer Show less  
Use the Predictive Intelligence Homepage (All \> Predictive Intelligence \> Homepage) to view, create, and manage your solutions.

## Key Features

* **Classification solutions:** Automatically categorize and route work during record creation by setting field values based on past records.
* **Similarity solutions:** Recommend resolutions by identifying similarities between new and existing records.
* **Clustering solutions:** Group similar records to uncover patterns and detect major incidents.
* **Regression solutions:** Previously used to predict numeric outputs (e.g., time to resolve incidents), creation of new regression solutions is no longer supported as of the Yokohama release; however, existing regression solutions can still be edited and trained.

## Training Data Selection

Effective solutions depend on high-quality training data. When selecting records to train your solution, ensure that:

* Input fields used by the solution are available and populated at record creation time.
* The output field is a choice field limited to a finite set of possible values for better accuracy.
* Training records contain only accurate, verified output values (exclude unreliable or recently modified records).
* There are multiple examples of each output field value to ensure broad coverage.
* Input fields include common variations to improve model robustness.

## Training Process and Data Sovereignty

Training is performed by exporting your solution definition and related records to a centralized training server located within the same datacenter as your instance. This ensures data does not leave the datacenter, supporting customers with data sovereignty requirements.

Once training is complete, the trained model is imported back to your instance and training data is deleted from the server.

Predictions are executed on a centralized prediction server within the same datacenter. The trained model artifacts are transferred to the prediction server on first use and cached thereafter for faster predictions. All data communication occurs securely over HTTPS within the datacenter firewall.

## Troubleshooting

For common training issues and troubleshooting guidance, refer to the Predictive Intelligence Common issues article in the Now Support Knowledge Base.  
Use one of the Predictive Intelligence (PI) frameworks to create and train
machine-learning solutions. Each framework delivers a different solution type for training the
system to predict, recommend, and organize data outcomes.

## Types of solutions {#training-ml-solutions__section_f3c_5zd_ntb}

The three PI frameworks provide different solutions that can be invoked by any application through a prediction API to make a prediction. Create and train your own solutions using your previous data. Navigate to AllPredictive IntelligenceHomepage to view and create solutions.  
Select the best framework for your desired prediction:

* Classification solutions: Sets field values during record creation to automatically
  categorize and route work based on past records. See [Create and train a classification solution](https://servicenow-prod.fluidtopics.net/vTeyKPuVzyw~LrMP0lg1zA "Specify the records used to train a classification solution, what fields trigger a prediction, and how often you want to retrain your solution.").

* Similarity solutions: Identifies similarities between new and existing records to
  recommend resolutions. See [Create and train a similarity solution](https://servicenow-prod.fluidtopics.net/sRwgscgENaw3ww5RnX2bMQ "Create and train a machine-learning solution to collect and compare your existing records to new similar records. For example, you can compare the text in an open Incident record to a resolved Incident record to reuse its resolution.").

* Clustering solutions: Groups similar records into clusters to identify patterns and
  major incidents. See [Create and train a clustering solution](https://servicenow-prod.fluidtopics.net/P3xDSJFvgfaECsuTVCcX6A "Group similar records into clusters so you can address them collectively or identify patterns.").

* Regression solutions:  
  Important:  
  With the Yokohama release, support for creating new regression solutions was removed. You can still edit and train existing regression solutions, but you won't be able to create new ones.
  Uses historic data to predict numeric outputs, such as estimating the time it takes to resolve an incident or case. See [Create and train a regression solution](https://servicenow-prod.fluidtopics.net/rqgWpoeQ5OCaB3Mpz_1sOA "Train your solution by using historical data to predict numeric outputs, such as a temperature or a stock price. For example, you can use regression to estimate the time it takes to resolve an incident or a case.").
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## Selecting data records for training your solution {#training-ml-solutions__section_gt5_cq1_v1b}

A solution is only as good as the record data you use to train it. In general, a good training dataset has these characteristics.

* The solution definition input fields are available to users when creating records. To make predictions at record creation, the solution must have the input field values at record creation.
* The solution definition output field is a choice field. To make more accurate predictions, limit the output field to a finite set of possible values.
* The training records only contain correct values for the output field. To make more accurate predictions, filter out any records that have unreliable output field values. For example, if recently closed incidents are subject to review and change for a month, filter out any recently closed incidents.
* The training records contain multiple examples of each output field value that you want the solution to predict. To provide more record coverage, include multiple examples of each output field value.
* The training records include common variations of the input fields. To provide more record coverage, include multiple examples of input field values.
{#training-ml-solutions__ul_cnb_cn4_j1b}

## Exporting your solution for training {#training-ml-solutions__section_tbw_kdh_j1b}

To train a solution, you export its solution definition and associated records to a
centralized training server within the same datacenter. When the training completes, the
training server exports the solution back to your instance and deletes all of your training data
from the server. As every datacenter has its own dedicated training server and the data doesn't
leave the datacenter, this service is also available to customers who have data sovereignty
requirements.  
Predictions occur on a centralized prediction server within the same datacenter as the instance. The trained model artifacts are sent from the instance server to the prediction server when the prediction is invoked for the first time. After that, the trained model artifacts are cached on the prediction server for subsequent predictions.  
Note:  
All communication between the instance and the training service occurs within the same datacenter firewall. Even so, all communications occur over HTTPS.

## Solution training troubleshooting {#training-ml-solutions__section_owf_zs1_ttb}

For troubleshooting common training issues, see the [Predictive Intelligence Common issues \[KB781893\]](https://support.servicenow.com/nav_to.do?uri=/kb?id=kb_article_view&sysparm_article=KB0781893)
article in the Now Support
Knowledge Base.

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