cookiecutter-mlops-package
🥈SilverCookiecutter-mlops-package automates the creation of Python packages and Docker images for MLOps tasks. Operations teams benefit from standardized templates for deploying machine learning models. It connects to CI/CD pipelines and container orchestration tools like Kubernetes.
435560Updated Today
Intermediate30min to implementautomation
Saves ~120 min per use
Quick InstallView Source
git clone https://github.com/fmind/cookiecutter-mlops-package.gitWorks with:
Claude
Overview
About This Skill
https://cookiecutter.readthedocs.io/
Use Cases
Quickly scaffold a new MLOps project with a predefined structure and best practices.
Automate the testing and quality checks of your machine learning code using integrated workflows.
Build and deploy Docker images for your MLOps applications seamlessly.
Manage dependencies and project tasks using PyInvoke for a more efficient development workflow.
Tags
Setup & Installation
Quick Install
No install command available. Check the GitHub repository for manual installation instructions.
Alternative Install (Git Clone)
git clone https://github.com/fmind/cookiecutter-mlops-packageRequirements
- Claude Code or compatible AI agent
- Works with: Claude
Quick Start Guide
1
Install the Skill
Copy the install command above and run it in your terminal.
2
Open Your AI Agent
Launch Claude Code, Cursor, or your preferred AI coding agent.
3
Try It Out
Use the prompt template or examples below to test the skill.
4
Customize
Adapt the skill to your specific use case and workflow.
Usage Examples
Prompt Template
Help me create a Python package for my MLOps project at [COMPANY] in the [INDUSTRY] industry. I need to include [DATA] and ensure it's ready for deployment with Docker.
Example Output
### Project Overview This Python package, named `mlops_project`, is designed to facilitate the deployment of machine learning models for real-time inference. ### Key Features - **Data Processing**: Includes modules for data cleaning and preprocessing. - **Model Training**: Integrates with popular libraries like TensorFlow and Scikit-learn. - **Docker Support**: A Dockerfile is provided for easy containerization. ### Installation Instructions 1. Clone the repository: `git clone https://github.com/yourusername/mlops_project` 2. Navigate to the directory: `cd mlops_project` 3. Build the Docker image: `docker build -t mlops_project .` ### Usage Once the Docker image is built, you can run the container using: ```bash docker run -p 5000:5000 mlops_project ``` This will start the API for model predictions.
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