Every premium and members-only resource for $9.99/month, as a no-commitment subscription. See the plans

PalateKit

Sign in
Menu

Request a resource

Describe the resource you are missing in one sentence. We will email you when it is published.

0 / 280

A page, a video or an image online.

Unlock PalateKit

Unlock everything

Plan

Paying means accepting our terms of sale.

Secure payment by Stripe. Receipts and invoices sent by email.

Welcome back

Sign in to install, download or save this resource. A free account is enough for free resources.

ML training pipelines: tracking, orchestration, registry v1.0.0

Use this resource to build production ML pipeline infrastructure

New

  • 477 downloads
  • 218 likes
  • 184 saves

Free

Install with the CLI

In a terminal, from the folder you want:

npx --yes palatekit@latest install mlops-training-pipelines

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 sets up experiment tracking with MLflow or Weights & Biases, writes Kubeflow or Airflow DAGs for training, designs a feature store with Feast and automates retraining and model validation. It also covers DVC for data versioning.

Mostly picked by

Updated November 2, 2026

Card for the “ML training pipelines: tracking, orchestration, registry” skill

Details

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

Similar resources

View all

What you get

  • references/training-pipelines.md and pipeline-orchestration.md: training DAGs and orchestration
  • references/experiment-tracking.md, feature-engineering.md, model-validation.md: tracking, features and validation

Good fit for

  • You automate weekly retraining of a model
  • Your ML experiments are not tracked anywhere

Not for

    Tags

    • mlops
    • mlflow
    • pipeline
    • training

    About

    You say

    “Write an Airflow DAG that trains the model every night, logs metrics to MLflow and blocks deployment below a threshold.”

    Card for the “ML training pipelines: tracking, orchestration, registry” skill

    Skill published by jeffallan 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 build production ML pipeline infrastructure
    3. Review and adjust the result

    Content preview

    Files

    • LICENSE1.1 KB
    • NOTICE-PALATEKIT.md0.5 KB
    • references/experiment-tracking.md22.2 KB
    • references/feature-engineering.md18 KB
    • references/model-validation.md30.7 KB
    • references/pipeline-orchestration.md23.9 KB
    • references/training-pipelines.md22.7 KB
    • SKILL.md7.4 KB

    Origin and licence

    Listed unmodified

    Author
    jeffallan
    Source
    github.com/jeffallan/claude-skills/tree/b7f220d32a0925c9bbfd24995e208e76bb74507c/skills/ml-pipeline
    Licence
    MIT

    Notice

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

    User reviews

    –

    No ratings yet

    Loading reviews…

    Delete your review?

    Your rating and comment will be removed from the page.