MLOps-Deployment

This repository focuses on MLOps (Machine Learning Operations) practices, tools, and strategies for deploying and managing machine learning models in production. It covers topics such as CI/CD pipelines for ML, model monitoring, experiment tracking, and infrastructure as code for ML workloads.

Topics

• CI/CD for Machine Learning
• Model Versioning and Registry
• Experiment Tracking (MLflow, Weights & Biases)
• Model Monitoring and Drift Detection
• Infrastructure as Code (Terraform, Ansible)

Installation

# Clone the repository
git clone https://github.com/Whilly3/MLOps-Deployment.git
cd MLOps-Deployment

# Install dependencies
pip install -r requirements.txt

Usage

Each project or example within this repository will have its own dedicated directory with detailed setup and usage instructions.

Contribution

Contributions are welcome! Please follow the contribution guidelines for submitting pull requests.

License

This project is licensed under the MIT License.