Explore Predictive Intelligence
Summarize
Summary of Explore Predictive Intelligence
ServiceNow® Predictive Intelligence integrates artificial intelligence and machine learning into ServiceNow applications, enhancing work experiences by enabling predictive capabilities. Customers can create and train models using three frameworks: classification, clustering, and similarity. These trained models are accessible through an API across ServiceNow applications.
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Key Features
- On-Premise Availability: Predictive Intelligence can be deployed on-premise. Interested customers should contact their account manager for details.
- Model Training: Customers can engage in supervised or unsupervised training to improve model predictions by providing appropriate data sets.
- Predictive Model Components: Includes solution definitions that specify input and output fields, training frequency, and precision metrics.
- Frameworks: Offers three frameworks for modeling: classification for categorization, similarity for finding related items, and clustering for grouping similar records.
Key Outcomes
Implementing Predictive Intelligence enables ServiceNow customers to enhance their operational efficiency by automating decision-making processes through accurate predictions. Solutions offer measurable precision and coverage, ensuring that a significant percentage of records receive predictions, thereby improving service delivery and responsiveness across various business units and industries.
ServiceNow® Predictive Intelligence is a platform function that provides a layer of artificial intelligence that empowers features and capabilities across ServiceNow® applications to provide better work experiences.
Overview of Predictive Intelligence
Predictive Intelligence is a powerful set of tools applying artificial intelligence and machine learning to make predictions. You can create and train models in three different frameworks: classification, clustering,
and similarity. A trained solution can be invoked by any ServiceNow application through an API.
To learn more about ways to use existing models, see Using Predictive Intelligence.
Predictive Intelligence for on-premise customers
Terminology
- Artificial intelligence
- Systems designed to do work that needs a level of human intelligence to accomplish.
- Machine learning
- Ability for models to improve over time with more experience.
- Models
- Collections of algorithms, math, and statistics that make predictions and decisions based on input-output data.
- Training
- Adding or changing data that the model is based on to affect future predictions.
- Supervised Training
- Providing input-out pairs so that the model can generate rules that connect the two.
- Unsupervised Training
- Providing raw data so that the model can identify structures in the data set.
- Training frequency
- How often models are retrained to incorporate new data into an existing model.
- Word corpus
- Vocabulary that a model can use to look for textual similarity.
Predictive model components
- Solution definition
- A data record you create and configure that specifies these values for training a predictive model.
- The records used to train the model. For example, only train on incidents that are resolved or closed within the last six months.
- The input fields that the model uses to make predictions. For example, use the incident short description to make a prediction.
- The output field whose value the model predicts. For example, set the incident category based on the short description.
- The frequency to retrain the model. For example, retrain the model every 30 days.
- Solution
- The solution is the result of a solution definition that you've trained in a ServiceNow datacenter. Predictive Intelligence uses the solution to predict a target field value given one or more input field values. All solutions specify these values.
- The solution precision is the aggregate percentage of correct predictions. For example, a precision of 50 means that out of 100 predictions, half of them should have the correct value.
- The solution coverage is the aggregate percentage of records that receive a prediction. For example, a coverage of 50 means half of all eligible records actually receive a prediction.
- The solution classes are the output field values for which the model can make predictions. Each class is an output field value with a list of possible precision, coverage, and distribution metrics to choose from. For example, the Incident Categorization solution has a class for each category such as software, inquiry, and database.
- The class distribution is the percentage of records from the entire table that have this particular output field value. For example, a distribution of 50 for the inquiry class means that half of incidents have the inquiry category.