When to use "build from scratch" vs a "template"?

This article explains the motivations behind choosing different application types in the AI & Analytics Engine.

In this article, you will learn the motivations behind choosing different application types in the AI & Analytics Engine. Specifically, when should you use the “build from scratch” option to create an app compared to the “template” option of creating an app.

Overview page of the Engine

Note: If you want to learn more about apps, read this article.


A template is a structured approach, designed to solve a specific business problem. It offers a more streamlined way of tackling the problem, by providing guidance through all the stages of the solution. This includes data ingestion, analysis, processing, modeling, deployment, and consumption of outputs.

For more information about what templates are, read this article

Template option in the Engine

Build from scratch

Build from scratch is a more manual method of creating a classification, regression or clustering app. It is not limited to any specific business problem. Since the process is manual, the user may need to perform additional tasks, such as creating data preparation recipes, or manually triggering a trained pipeline for predictions outside the platform etc. 

Build from scratch

For more information about what "build from scratch" is, read this article.

When to use “build from scratch” vs “template”?

If a template that matches the user’s business problem is available on the platform, it’s recommended that the user chooses the template for the following reasons: 

  • It is easier to use.

    • It guides the user through all steps

    • Automatically prepares the data and trains one or more models

    • May have additional ML-Ops capabilities depending on the business requirements of the template

  • Requires less time to solve the problem from end to end due to extensive automation

If none of the templates suits the user’s particular business requirement, the user should choose the “build from scratch” option instead. The main advantage of this option is its flexibility: 

  • Can use any tabular datasets and schemas

  • Control over data preparation recipes

  • Manual selection of models for training

Whenever possible, the user is recommended to choose the “template” for ease of use. Otherwise, the user can choose the “build from scratch” option for more flexibility and customisation capability.