What is a template?

This article explains the concept of templates in the AI & Analytics Engine

The AI & Analytics Engine provides templates to automatically build complete machine learning solutions and pipelines from start to finish, resulting in consumable outputs that are ready for integration into production systems.

Each template is tailored to a specific use case such as “customer churn prediction”, “customer lifetime value prediction”, “next-best offer optimization”, “demand forecasting”, which are targeted at specific industries such as banking, telecommunications, digital marketing, etc.

Inputs required by a template

Templates require users to provide only non-technical inputs – such as the business problem definition (e.g. how do you define churn?), domain-knowledge inputs (where/in which time frames can potential signals about customer’s future intent be found), and connections to their data sources (e.g. files, database/data warehouse/data lake connections, etc.).

The Engine then automatically builds a full end-to-end solution that is ready for consumption.

An illustration of how the AI & Analytics Engine builds an end-to-end solution with the customer churn prediction template

Example:

The Customer Churn Prediction template requires users to provide certain inputs, as shown in the table below. The inputs vary depending on the type of business: subscription or transactional.

The subscription option is appropriate for enterprises that offer recurring services, such as telecom, online video streaming, etc.
The transactional option is relevant for enterprises that have customers who make individual purchases or transactions, such as retail, banking, etc.

	Transactional	Subscription
Datasets	Transactions Customer information (optional)	Subscription start and churn dates Event logs Customer information (optional)
Definitions about churn prediction	How many days in advance is enough to apply retention actions? For the transactional option: How much inactivity and over what time frame indicates churn? For the subscription option: What period of the predicted subscription expiry date signals the need for preventive actions?
Domain-knowledge inputs	What time windows usually contain early signal of churn? Which characteristics of customers might help the churn prediction? For the subscription option: Which values in the event logs could influence the likelihood of churn?

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The template provides a way for users without technical data science or ML background to tap into the power of machine learning. It only requires users to convey their business problem and provide domain-knowledge inputs.

For more information about the customer churn prediction template, read this article.

Benefits of using the AI & Analytics Engine’s templates:

It provides a complete repeatable, maintainable, auditable, and fully production-ready machine learning workflows
It speeds up the process of bringing ML to production and getting to predictive insights and value from data rather quickly
It increases productivity for both non-technical and technical users:
- It automates mundane tasks in the ML workflow
- It allows technical users to focus on high value tasks such as:
  - Business-problem formulation
  - Applying domain expertise