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.

6431010Updated 1w ago

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/

How to Use

1. Identify the specific skills and tools your team needs to focus on for the MLOps course. 2. Customize the prompt template with the team size, company name, specific skills, and tools. 3. Copy-paste the customized prompt into Claude or ChatGPT to generate the course outline. 4. Review the generated outline and make any necessary adjustments to fit your team's needs. 5. Use the outline to structure the 5-day course, ensuring each day's objectives and exercises are clearly defined.

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

Create a 5-day MLOps coding course outline for [TEAM SIZE] engineers at [COMPANY]. Include daily learning objectives, hands-on coding exercises, and integration points with [TOOLS]. Focus on [SPECIFIC SKILLS] like CI/CD pipelines, model monitoring, and automated retraining. Provide a list of required Python libraries and Claude agent integrations.

Example Output

Day 1: Introduction to MLOps Principles
Objective: Understand the core concepts of MLOps and its importance in modern ML workflows.
Activity: Set up a Python environment with essential libraries (scikit-learn, TensorFlow, PyTorch).
Exercise: Create a simple ML model and deploy it using a Claude agent.

Day 2: CI/CD for ML Pipelines
Objective: Learn to implement Continuous Integration and Continuous Deployment for ML models.
Activity: Set up a GitHub repository and configure CI/CD pipelines using GitHub Actions.
Exercise: Automate the testing and deployment of the ML model created on Day 1.

Day 3: Model Monitoring and Logging
Objective: Implement monitoring and logging for ML models to ensure performance and reliability.
Activity: Use tools like Prometheus and Grafana to monitor model performance.
Exercise: Set up alerts for model drift and performance degradation.

Day 4: Automated Retraining and Model Versioning
Objective: Learn to automate the retraining of ML models and manage different versions.
Activity: Use MLflow to track experiments and manage model versions.
Exercise: Automate the retraining process based on new data.

Day 5: Integration with Claude Agents
Objective: Integrate Claude agents into the MLOps workflow for enhanced automation.
Activity: Set up Claude agents to handle specific tasks in the ML pipeline.
Exercise: Automate the deployment and monitoring of ML models using Claude agents.

Required Python Libraries: scikit-learn, TensorFlow, PyTorch, MLflow, Prometheus, Grafana
Claude Agent Integrations: Model deployment, monitoring, and automated retraining