FreeSkills
Commits, branches and sync with the but CLI
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Works with
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Put machine learning models into production v1.0.0
Use this resource to put machine learning models into production with serving, A/B tests and monitoring
New
- 306 downloads
- 217 likes
- 150 saves
Free
Install with the CLI
In a terminal, from the folder you want:
npx --yes palatekit@latest install production-ml-systemsWith 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 loginThe agent designs a production ML system: training and optimization, model serving, feature engineering, MLOps and CI/CD integration, monitoring and A/B testing. It draws on PyTorch 2.x, TensorFlow and common ML frameworks.
Mostly picked by
Updated November 2, 2026
Previews
Details
- Install guides
- Generates with
- Your agent's model
- Licence
- MIT
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What you get
- SKILL.md: ML frameworks and model deployment
- SKILL.md: feature engineering, training, production infrastructure and monitoring
Good fit for
- You must serve a prediction model behind an API
- You want to compare two model versions in production
Not for
Tags
- machine learning
- pytorch
- mlops
- production
About
You say
“Deploy this fraud detection model behind an API and plan an A/B test.”
Skill published by davila7 under the MIT license, delivered in its original version.
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How to use it
- Install the skill in your agent's skills folder
- Ask your agent to put machine learning models into production with serving, A/B tests and monitoring
- Review and adjust the result
Content preview
Files
- LICENSE1.1 KB
- NOTICE-PALATEKIT.md0.5 KB
- SKILL.md9.2 KB
Origin and licence
Listed unmodified
- Author
- davila7
- Source
- github.com/davila7/claude-code-templates/tree/f57964ebda91cdfb31755661ecea9395f30020f1/cli-tool/components/skills/ai-research/ml-engineer
- Licence
- MIT
Notice
« ml-engineer » (davila7, MIT) livré dans sa version d'origine : fiche en français et en anglais par PalateKit.
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