mlops-coding-course

🥈Silver

Learn to create, develop, and maintain a state-of-the-art MLOps code base. Ideal for operations teams looking to streamline ML workflows. Connects to Python and integrates with Claude agents.

6371010Updated Today

Intermediate30min to implementautomation

Saves ~180 min per use

Quick InstallView Source

git clone https://github.com/MLOps-Courses/mlops-coding-course.git

Works with:

Claude

Overview

About This Skill

https://mlops-coding-course.fmind.dev/

Use Cases

Automate the setup of a development environment for MLOps projects using Python.

Implement CI/CD workflows to streamline the deployment of machine learning models.

Utilize Jupyter notebooks for rapid prototyping and initial model assessments.

Enhance code quality through automated linting, testing, and debugging practices.

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/MLOps-Courses/mlops-coding-course

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Can you provide an overview of the MLOps coding course for [COMPANY] in the [INDUSTRY]? Please include key topics covered and expected outcomes based on [DATA].

Example Output

### MLOps Coding Course Overview

The MLOps coding course offered by **Tech Innovations Inc.** in the **AI Development** industry focuses on essential skills for integrating machine learning into production. Key topics include:
- **Version Control for ML Models**: Learn to use Git for tracking changes in ML code.
- **Continuous Integration/Continuous Deployment (CI/CD)**: Implement pipelines for automated testing and deployment.
- **Monitoring and Maintenance**: Understand how to monitor model performance in real-time.

By the end of the course, participants will be able to build robust MLOps pipelines and ensure their ML models are production-ready, leading to improved operational efficiency and reduced time to market.