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ZenML Codespace Tutorial

Interactive VS Code extension that provides hands-on ZenML pipeline tutorials directly in your development environment
Project
ZenML Codespace Tutorial
project id
zenml-codespace-tutorial
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Pipelines

Tutorial Pipeline Examples

Collection of 10 progressive ZenML pipeline examples covering core MLOps concepts

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  • Orchestrator: default
  • Artifact Store: default
Details

The ZenML VS Code Tutorial Extension provides an interactive learning experience for ZenML directly within your development environment. Perfect for developers who want to learn MLOps concepts through hands-on coding exercises.

What It Does

This extension transforms your VS Code workspace into an interactive ZenML learning environment with:
- 10 progressive tutorial modules covering core ZenML concepts
- Interactive code examples you can run and modify
- Step-by-step guidance through MLOps pipeline development
- Built-in ZenML dashboard integration
- Custom VS Code theme optimized for ZenML development

Tutorial Modules

  1. Hello World - Learn the basics of Steps & Pipelines
  2. Step I/O - Master typed inputs and outputs between pipeline steps
  3. Parameters - Make your pipelines configurable and flexible
  4. Tagging - Organize and track your pipeline runs effectively
  5. Metadata - Record and visualize important pipeline metrics
  6. Caching - Optimize pipeline performance with smart caching
  7. Visualizations - Create automatic and custom data visualizations
  8. Fan-out/Fan-in - Handle parallel processing workflows
  9. Retries & Hooks - Build robust, production-ready pipelines
  10. YAML Config - Separate configuration from code for better maintainability

How It Works

  • Automatically activates when you open a VS Code workspace
  • Provides interactive tutorials through a custom webview interface
  • Includes runnable Python pipeline examples for each concept
  • Integrates with ZenML dashboard for real-time pipeline monitoring
  • Supports both GitHub Codespaces and local development containers
Gallery

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Dashboard displaying machine learning models, including versions, authors, and tags. Relevant to model monitoring and ML pipelines.