Explore Predictive Intelligence
Summarize
Summary of Explore Predictive Intelligence
ServiceNow® Predictive Intelligence enhances applications with artificial intelligence and machine learning, facilitating improved work experiences. It allows users to create and train predictive models using frameworks such as classification, clustering, and similarity. Models can be accessed by any ServiceNow application via an API.
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Key Features
- On-Premise Availability: Predictive Intelligence can also be deployed on-premise; interested customers should contact their account manager for details.
- Terminology: Key terms include artificial intelligence, machine learning, models, and training methods (supervised and unsupervised).
- Predictive Model Components: Models consist of a solution definition that specifies training data, input and output fields, and retraining frequency.
- Frameworks: The Zurich release includes three frameworks for different prediction types: classification, similarity, and clustering.
Key Outcomes
Implementing Predictive Intelligence allows for more accurate predictions, improving incident categorization and operational efficiency. Solutions trained in the platform provide metrics such as precision and coverage, enabling organizations to gauge the effectiveness of their predictive models.
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.