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What is pipeline?

1. Introduction​

  • Definition of a pipeline
  • Real-world analogy (e.g., factory conveyor belt, coffee brewing)
  • Why pipelines are useful: automation, consistency, reproducibility
  • Who uses pipelines (e.g., data scientists, creatives, developers)

2 Inputs​

  • What are inputs in a pipeline?
  • Types of inputs: text, images, audio, video, JSON, numbers
  • Example: Uploading a selfie or entering a prompt
  • Input validation and customization

3 Outputs​

  • What are outputs?
  • How outputs are generated by the pipeline
  • Output formats (e.g., video, audio, image, structured data)
  • Post-processing and download options

4 Environment​

  • Key-value pairs of variables used to control behavior within the pipeline.
  • Store secrets (API keys, tokens)
  • Configure services (e.g., model endpoints, region settings)

5 Steps & Nodes​

  • Each step = a node
  • Common step types: AI model call, fetch data via HTTP, etc.
  • Connecting steps logically

6. Playground​

  • What is the Playground?
  • Interactive UI to test and run pipelines
  • How users can tweak inputs and rerun
  • Sharing results or embedding
  • Example walkthrough: Upload photo → Choose style → Generate image

7. Use Cases​

  • Simple examples:
    • Generate a cartoon avatar from a photo
    • Transcribe and translate a video
  • Business examples:
    • Generate social media content from a product image
    • Automated background removal and color correction

8. FAQs​

  • Can I share pipelines?
  • Can pipelines fail?
  • How do I know which models are used?
  • Can I export the result?

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