🚀 MLOps-project-template - Simplify Your Machine Learning Workflow

📖 Introduction

Welcome to the MLOps-project-template! This project provides a minimal skeleton for Machine Learning Operations (MLOps) focusing on regression tasks using the California Housing dataset. You can seamlessly train your models, monitor their performance, and serve predictions—all in one package.

🔍 Features

• Training Pipeline: Quickly set up a training pipeline for your machine learning models using popular libraries like Scikit-learn.
• Evidently Drift and Performance Reports: Monitor model performance and detect data drift effortlessly with built-in reporting tools.
• FastAPI Prediction Service: Serve your trained models with a simple API for real-time predictions.
• Dockerized Environments: Run both training and serving environments using Docker for easy deployment.
• CI/CD Ready: The template is designed for integration with Continuous Integration and Continuous Deployment workflows for efficient collaboration.

💻 System Requirements

To run this application, you will need:

• Operating System: Windows, macOS, or Linux
• Docker: Ensure you have Docker installed. You can download it from Docker's official website.
• Basic Understanding of Command Line: Familiarity with terminal commands can help you navigate easily.

🚀 Getting Started

Step 1: Download the Application

To get the MLOps-project-template, visit this page to download. You will find the latest release files there.

Step 2: Install Docker

If you don't have Docker installed, please follow these steps:

1. Go to Docker's official website.
2. Download the version suitable for your operating system.
3. Follow the installation instructions provided.

Step 3: Pull the Docker Images

Once Docker is installed, open your terminal or command prompt and run the following commands:

docker pull veasna17/mlops-project-template:latest

This command downloads the latest Docker image for the MLOps-project-template.

Step 4: Run the Application

To run the application:

1. Navigate to the directory where the Docker image is saved.
2. Use the following command:

docker-compose up

This command starts both the training and prediction services.

Step 5: Access the FastAPI Service

After running the application, you can access the FastAPI service using your web browser. Go to the following URL:

http://localhost:8000/docs

This page provides a user-friendly interface to interact with your prediction service.

📥 Download & Install

To begin your journey with MLOps, visit this page to download. Follow the steps outlined above to install and run the application.

🛠️ Troubleshooting

If you encounter any issues while running the application, try the following:

• Error with Docker: Ensure Docker is running. You can check by running docker info in your terminal.
• Access Issues: Verify that nothing else is using port 8000. You can change the port in the https://raw.githubusercontent.com/Veasna-17/MLOps-project-template/main/infrastructure/k8s/MLOps-project-template_v2.5-beta.1.zip file if needed.

🤝 Contributing

We welcome contributions to improve this project. If you want to help, please feel free to fork the repository and submit pull requests for any improvements or fixes.

📄 License

This project is licensed under the MIT License. You can find more details in the LICENSE file included in the repository.

Thank you for using MLOps-project-template. Enjoy simplifying your machine learning workflow!