MLOps-Model-Deployment

A Go-based project for deploying machine learning models as microservices, focusing on efficiency and scalability.

Overview

This repository contains a Go-based solution for deploying machine learning models as high-performance microservices. It emphasizes efficient model serving, API development, and containerization strategies for MLOps workflows.

Features

• Fast API Endpoints: Develop low-latency RESTful APIs for model inference.
• Containerization: Dockerfile for easy packaging and deployment.
• Scalability: Designed for horizontal scaling to handle high request volumes.
• Model Agnostic: Can serve models from various frameworks (e.g., TensorFlow, PyTorch) via ONNX or custom serialization.

Installation

git clone https://github.com/Theoplad9/MLOps-Model-Deployment.git
cd MLOps-Model-Deployment
# Ensure Go is installed (https://golang.org/doc/install)

Usage

# Build the application
go build -o model-server main.go

# Run the server
./model-server

# Example API call (assuming model is loaded and endpoint is /predict)
# curl -X POST -H "Content-Type: application/json" -d '{"data": [1, 2, 3]}' http://localhost:8080/predict

Project Structure

MLOps-Model-Deployment/
├── cmd/
│   └── server/
│       └── main.go
├── internal/
│   ├── handler/
│   │   └── predict.go
│   └── model/
│       └── loader.go
├── Dockerfile
├── go.mod
├── go.sum
├── README.md
└── LICENSE

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for details.

License

This project is licensed under the MIT License - see the LICENSE file for details.