Qlik Predict Features

Supports a variety of scripting environments

Mines data from databases and prepares data for analysis

Applies statistical algorithms to selected data

Analyzes both structured and unstructured data

Interacts with data to prepare it for visualizations and models

Offers modeling capabilities

Creates data visualizations or graphs

Generates reports of data performance

Unifies information on a singular platform

Offers the ability for developers to drag and drop pieces of code or algorithms when building models

Provides users with pre-built algorithms for simpler model development

Supplies large data sets for training individual models

Transforms raw data into features that better represent the underlying problem to the predictive models

Offers natural language processing services

Offers natural language generation services

Offers natural language understanding services

Manages the intelligent application for the user, reducing the need of infrastructure

Allows users to insert machine learning into operating applications

Provides easily scaled machine learning applications and infrastructure

Gives user ability to import a variety of data sources for immediate use

Allows users to generate text based on a text prompt.

Condenses long documents or text into a brief summary.

Allows users to generate text based on a text prompt.

Condenses long documents or text into a brief summary.

Provides the ability to generate images from a text prompt.

Capability to perform complex tasks without constant human input

Ability to break down and plan multi-step processes

Works across multiple software systems or databases

Improves performance based on feedback and experience

Engages in human-like conversation for task delegation

Anticipates needs and offers suggestions without prompting

Makes informed choices based on available data and objectives

Analyzes incoming datasets to surface missing values, distributions, outliers, and data quality issues automatically

Enables users to ingest data from diverse sources (databases, APIs, cloud storage, spreadsheets) without custom coding

Automatically alerts users when incoming data’s schema deviates from expected structure over time

Suggests or auto‑selects candidate algorithms and hyperparameters based on dataset characteristics

Allows users to insert custom code (e.g. Python, R, SQL) or custom modules into pipeline stages for flexibility

Automatically proposes or applies derived features to improve model performance