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Machine learning pipeline builder
Describe your data and goal. Get a repeatable pipeline from data prep to training, checks and release.
Machine learning pipeline builder is a free AI skill for the job "Build and ship software". Give the instructions to an AI assistant that supports the required files and tools, then review its output.
Free · No account · MIT licence · Made by Burhan Sebin · Level: expert · Thinking: high
Made by Burhan Sebin. See the original on GitHub; we copied it as it is, with its licence.
What it does
A machine learning pipeline you can run again next month and get the same answer. Every stage, from raw data to a released model, has a named input, a named output and a version. First it looks at what you already use for code, storage and scheduling, and reuses it. Then it records your goal, your baseline and the score a model must reach. It draws the stages as a graph, adds data checks, sets the pass or fail gate before release, and plans retries and rollback. It finishes by testing the pipeline and writing down what passed.
You give it
- Your goal and the score a model must reach
- Where the data lives and how often it changes
- Your current training and evaluation code
- Where the model will run
- Compute, cost or privacy limits (optional)
- Codebase (optional)
You get
- A stage-by-stage pipeline graph
- Pipeline code in your existing tools
- Input and output schemas for each stage
- Pass or fail rules for release
- Retry, backfill and rollback steps
Use it when
- You have a notebook that works once and breaks when someone else runs it.
- You need a model release that only happens when it beats a set score.
- You must explain which data and code produced the model in production.
- You want retries and a rollback plan before the first failure, not after.
How to use it
- Add it to your AI once: download it and add the folder to Claude, ChatGPT or Codex as a skill.
- Ask for the job in your own words. Your AI opens the skill when it fits.
- It works best when your AI can run code and read your project files.