QuickML Help Documentation
Explore Catalyst QuickML, a fully no-code ML pipeline builder service in the
Catalyst development platform for creating machine-learning pipelines with end-to-end solutions.
Get Started
Access a quick start guide to learn about the basics of Catalyst QuickML, its components, and their real-world use cases.
Tutorials
Access step-by-step tutorials to build sample applications and microservices using Catalyst QuickML components.
Release Notes
Stay updated with the latest news on feature releases, enhancements, and bug fixes made in Catalyst QuickML.
Features
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Data Preprocessing
Use data preprocessing techniques as stages in the pipeline building process to improve machine learning models.
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.
Dataset Extraction
Utilize dataset operations like Select Data Set and Split Dataset to divide or merge two datasets.
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ML Algorithms
QuickML offers various classification and regression algorithms and operations for pipeline execution.
Zia Features
QuickML integrates a broad range of Zia AI features such as Keyword extraction to deliver analytical results.
ML Operations in QuickML
Use ML operators like Encoding, Transformers, and more available in QuickML for data preprocessing.
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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 Metrics Viewer
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.
Data
Machine Learning
QuickML Pipelines
Data Preprocessing
Use data preprocessing techniques as stages in the pipeline building process to improve machine learning models.
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.
Dataset Extraction
Utilize dataset operations like Select Data Set and Split Dataset to divide or merge two datasets.
ML Algorithms
QuickML offers various classification and regression algorithms and operations for pipeline execution.
Zia Features
QuickML integrates a broad range of Zia AI features such as Keyword extraction to deliver analytical results.
ML Operations in QuickML
Use ML operators like Encoding, Transformers, and more available in QuickML for data preprocessing.
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 Metrics Viewer
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.
Deployments and Billing
Learn about deploying your project to production from the sandbox, setting up your payment method, and managing billing.
Environments
Explore the development and production environments in Catalyst, and learn about project deployments and working with both environments.
Billing
Learn about the various payment methods and pricing plans in Catalyst, accessing billing and transaction reports, configuring budgets, and more.
FAQ
Browse through frequently asked questions and find quick solutions to common queries and challenges on working with Catalyst QuickML.