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Digital Hub

Pipelines

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Digital Hub

Introduction
Overview and architecture
Quick Start
Installation
Concepts and Tasks
Concepts and Tasks
Components
Components
- Landing Page
- Core UI
- Kubernetes Resource Manager
- KubeFlow Pipelines
- Interactive Workspaces
  Interactive Workspaces
  - Jupyter
  - Dremio
  - SQLPad
  - Grafana
  - Visual Studio Code
Scenarios
Scenarios
- Extract-Transform-Load
  Extract-Transform-Load
- DBT transformation
  DBT transformation
  - ETL with digitalhub-core and DBT scenario introduction
  - Workflow
- MLOps: Scikit-learn ML models
  MLOps: Scikit-learn ML models
- MLOps: MLflow ML models
  MLOps: MLflow ML models
  - MLFLow ML scenario introduction
  - Deploy and expose the model
- MLOps: Large Language Models
  MLOps: Large Language Models
  - Managing LLM Models
- MLOps: Custom ML model training
  MLOps: Custom ML model training
  - Custom ML scenario introduction
  - Deploy and expose the model
- Dremio transformation
  Dremio transformation
  - Data transformation and usage with Dremio and Grafana
- API via PostgREST
  API via PostgREST
Runtimes reference
Runtimes reference
- Python
- DBT
- Container
- Pipelines Pipelines
  Table of contents
  - Management with SDK
- Modelserve
SDK Reference
SDK Reference
- Python SDK Reference

Table of contents

Management with SDK

KFP Pipelines Runtime

The kfp runtime allows you to run workflows within the platform.

Management with SDK

Check the SDK KFP runtime documentation for more information.