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QuickML Help Documentation

Explore Catalyst QuickML, a fully no-code ML pipeline builder service in the
Catalyst development platform for creating end-to-end machine learning solutions.



  1. Data Connectors

    Import data from local or cloud storage solutions to use in QuickML for ML and analytics purposes.

    Data Profiling and Viewing

    Analyze raw data for structure, content, and relationships to enhance the date quality through data profiling.

    QuickML Visualization

    QuickML visualization charts will provide a clearer insight into your data visually.

    Data Preprocessing

    Use data preprocessing techniques as stages in the pipeline building process to improve machine learning models.

    Dataset Extraction

    Utilize dataset operations like Select Data Set and Split Dataset to divide or merge two datasets.

  2. Machine Learning

  3. Zia Features

    QuickML integrates a broad range of Zia AI features such as Keyword extraction to deliver analytical results.

    ML Algorithms

    QuickML offers various classification and regression algorithms and operations for pipeline execution.

    ML Operations in QuickML

    Use ML operators like Encoding, Transformers, and more available in QuickML for data preprocessing.

  4. QuickML Pipelines

  5. Pipeline Builder Interface

    Build pipelines with an easy-to-use pipeline builder with drag-and-drop elements for various operations.

    Creating ML Pipelines

    Understand the basics of pipeline creation process, and create and configure your first QuickML pipeline.

    Pipeline Execution Metrics

    View pipeline execution information, runtime details, and other metrics of your configured QuickML pipelines.

    Pipeline Endpoints

    Access the models you create with secure and authenticated endpoints, and test them before deploying.

Developer Tools


Browse through frequently asked questions and find quick solutions to common queries and challenges on working with Catalyst QuickML.

Do I need to know coding to use Catalyst QuickML
No, Catalyst QuickML is a fully no-code ML pipeline builder service in the Catalyst development platform for creating machine-learning pipelines with end-to-end solutions.
What is the difference between AutoML and QuickML?
AutoML enables you to easily analyse a set of training data and generate predictive analytics on the dataset without requiring you to be involved in the complex ML training process that involves selecting the right ML algorithms to train the model, preprocessing or profiling the data, or managing the models. Catalyst implements the required model training, and automates the entire process for you. QuickML on the other hand provides you more control in managing ML and data operations, and lets you build, test, deploy, and monitor effective ML models end-to-end. You will be able to perform a host of data preprocessing and transformation operations, pick the ML algorithms for training, and design the pipeline exactly as you need, all with no coding involved.
What is an ML pipeline?
Machine-learning pipelines are the end-to-end execution of workflows for data and machine learning tasks, designed to orchestrate fully trained and accurate machine learning models to help provide predictive intelligence in a wide range of business requirements. QuickML has a unique no-code pipeline builder platform in which machine learning pipelines are designed and executed.
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