MLOps-Streamline

MLOps-Streamline is a production-ready CI/CD pipeline for machine learning models using Docker, Kubernetes, and MLflow. It provides a comprehensive and automated workflow for building, testing, and deploying machine learning models at scale, ensuring their reliability and performance in production environments.

Key Features

• Automated CI/CD: Streamlined pipeline for building, testing, and deploying machine learning models.
• Docker & Kubernetes Support: Seamless integration with Docker and Kubernetes for containerization and orchestration.
• MLflow Integration: Robust model versioning, tracking, and management using MLflow.
• Scalable Architecture: Designed to handle large-scale machine learning workloads and complex models.
• Comprehensive Monitoring: Detailed monitoring and logging for machine learning models in production.

Getting Started

Prerequisites

• Docker 20.10+
• Kubernetes 1.20+
• MLflow 1.20+
• (Optional) NVIDIA GPU with CUDA support for enhanced performance

Installation

git clone https://github.com/FunctionFlow1/MLOps-Streamline.git
cd MLOps-Streamline
pip install -r requirements.txt

Usage Example (Python)

import mlops_streamline as ms

# Initialize the MLOps-Streamline pipeline
pipeline = ms.MLOpsStreamline(config_path='config.yaml')

# Build and test a machine learning model
pipeline.build_and_test(model_name='my_model', dataset_path='data.csv')

# Deploy the model to a Kubernetes cluster
pipeline.deploy(model_name='my_model', cluster_name='my_cluster')

# Monitor the model in production
pipeline.monitor(model_name='my_model')

Contributing

We welcome contributions from the community! Please read our Contributing Guidelines for more information.

License

MLOps-Streamline is released under the MIT License.