What Model Types Can You Use to Create a Custom Model?

Correct Answers: A, B and D

AI Builder provides prebuilt and custom models in three categories: Text, Prediction, and Vision.

You can train the following model types: Text category: Category classification and Entity extraction.

Prediction category: Prediction.

Vision category: Form Processing, Object Detection.

Three of these types don't have prebuilt models: Prediction, Form Processing, Object Detection.

The other two, Category classification and Entity Extraction, have both model types - prebuilt and custom.

Several models have only the prebuilt type, such as Key phrase extraction, Sentiment Analysis, Business card reader, and others.

Options C and E are incorrect because Key phrase extraction and Sentiment Analysis are prebuilt models only.

For more information about custom AI builder models, please visit the below URLs:

AI Builder is a low-code artificial intelligence (AI) platform that enables users to create and deploy custom AI models without any prior coding experience. In AI Builder, you can create several types of custom models to meet your business requirements. Here's a detailed explanation of each model type:

A. Prediction: The prediction model is used to forecast numerical values based on existing data. For example, you can use the prediction model to predict the future sales of a product based on its historical sales data.

B. Object detection: The object detection model is used to identify and locate objects in images or videos. For example, you can use the object detection model to detect and track the movements of vehicles in a traffic surveillance video.

C. Key phrase extraction: The key phrase extraction model is used to identify important keywords and phrases in text data. For example, you can use the key phrase extraction model to extract the most relevant keywords from customer feedback surveys.

D. Category classification: The category classification model is used to classify text data into pre-defined categories. For example, you can use the category classification model to classify customer support tickets into different categories, such as technical issues, billing issues, or product feedback.

E. Sentiment analysis: The sentiment analysis model is used to determine the sentiment or emotion expressed in text data. For example, you can use the sentiment analysis model to analyze customer feedback and determine whether it is positive, negative, or neutral.

In summary, to create a custom AI Builder model, you can choose from the five model types mentioned above, based on your business requirements and data sources.