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End-to-end MLOps pipeline design v1.0.0

Use this resource to design an end-to-end MLOps pipeline

New

  • 581 downloads
  • 193 likes
  • 114 saves

Free

Install with the CLI

In a terminal, from the folder you want:

npx --yes palatekit@latest install mlops-pipeline-design

With no option, the skill goes to ~/.agents/skills/; add --agent claude-code (or codex, cursor…) to target an agent.

First time: connect the CLI with

npx --yes palatekit@latest login

The agent organizes the phases of a machine learning project: data analysis, training, deployment, monitoring and improvement. You get an MLOps roadmap with success criteria and expected deliverables.

Mostly picked by

Updated November 2, 2026

Card for the “End-to-end MLOps pipeline design” skill

Details

Works in
Install guides
Generates with
Your agent's model
Licence
MIT

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What you get

  • SKILL.md: phases, from data analysis to production monitoring
  • SKILL.md: configuration options, success criteria and deliverables

Good fit for

  • You need to industrialize a model that is already prototyped
  • You want to track model drift in production

Not for

    Tags

    • mlops
    • machine learning
    • deployment

    About

    You say

    “Design the MLOps pipeline for our recommendation model: weekly training, deployment and drift monitoring.”

    Card for the “End-to-end MLOps pipeline design” skill

    Skill published by rmyndharis under the MIT license, delivered in its original version.

    Show more

    How to use it

    1. Install the skill in your agent's skills folder
    2. Ask your agent to design an end-to-end MLOps pipeline
    3. Review and adjust the result

    Content preview

    Files

    • LICENSE1 KB
    • NOTICE-PALATEKIT.md0.5 KB
    • SKILL.md10.6 KB

    Origin and licence

    Listed unmodified

    Author
    rmyndharis
    Source
    github.com/rmyndharis/antigravity-skills/tree/364ea63c190fcf7cac035f7fa91f0406fa392584/skills/machine-learning-ops-ml-pipeline
    Licence
    MIT

    Notice

    « machine-learning-ops-ml-pipeline » (rmyndharis, MIT) livré dans sa version d'origine : fiche en français et en anglais par PalateKit.

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