1. Predictive Intelligence It is based on Machine Learning(ML) and Artificial Intelligence(AI)

2. Predictive Intelligence(PI) in ServiceNow It is the now platform implementation of AI and ML that makes the predictions based on the recommendations based on user input. Predictive Intelligence is a ServiceNow® platform capability that operationalizes machine learning solutions within your existing processes without the need for an army of data scientists to build custom solutions. Predictive Intelligence uses machine learning, natural language processing (NLP), a n d d e e p learning techniques to quickly analyze and compare records across all ServiceNow applications

3. PI can determine the following: Category Recommend Action Discover Hidden Patterns and content.

4. Applications Solution: Solution for PI: (In ITSM) 1. End user:(Employee customer) Virtual Agent Powered by NLU. 2. Frontline workers: similar open incidents similar closed incidents Recommended KB for an incidents incidents assignment and categorization 3. Service owner: Major incident detection Performance Analytics Natural Language Query

5. Today ServiceNow predictive intelligence offers you four different types of framework: Classification:Use machine-learning algorithms to set field values during record creation, such as setting the incident category based on the short description. You can train predictive models so that they act as an agent to automatically categorize and route work based on your past record-handling experience. Clustering: Group similar records into clusters so that you can address them collectively or identify patterns. For example, you can group similar incidents that have occurred recently to identify major incident. Similarity: Use this framework to identify existing records that are similar in text and context to an incoming record. For example, the framework can find similar incident records that have been resolved in the past to help an agent resolve the current incident. Build a collection of words that functions as the vocabulary the system uses to compare your trained records based on their textual similarity. Train all default and untrained similarity solutions Regression: Regression is a machine-learning framework that you can train with historic 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.

6. High Level Architecture Primary person involved in PI are: Application admin and report user. Additionally PI has two roles: 1. ml_admin: Application admin. 2. ml_report_user: Report user can view dashboard.

7. Tables: ML Solution Definition: used to store the data record used to train the classification, similarity and clustering solutions. ML Solution: Stores the list of solutions, which are produced after the solution definition is processed in ServiceNow’s training server. Class Confidence : Stores the list of classes used in predictions. Primarily used for classification framework. Precision Coverage LookUp: Stores the list of precision. Precision : The proportion of positive identification that is actually correct

8. PI General Terminology Artificial Intelligence Machine Learning ML Capabilities Supervised and Unsupervised ML

9. Solution Definations Word corpus:The word corpus acts as the vocabulary to aid in the prediction, a collection of words and text which Predictive Intelligence uses to learn similarity to help the agent find similar records. Training Frequency:When creating a Predictive Intelligence solution, all solution types have a Training Frequency field, where you select a retraining option from once daily or every 30 days in 3 month increments up to 180 days.

10. PI Framework Terminology Classification: 1. Estimated Precision 2. Class Distribution 3. Estimated coverage 4. Class 5. Estimated Recall

11. Classification Framework

12. Regression: Output/Output Range Confidence Threshold Mean absolute Value(MAE) Mean Absolute Percentage Error(MAPE)

13. Regression Framework

14. Similarity: Threshold

15. Clustering