https://github.com/techiescamp/mlops
MLOPS Projects
https://github.com/techiescamp/mlops
Last synced: over 1 year ago
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MLOPS Projects
- Host: GitHub
- URL: https://github.com/techiescamp/mlops
- Owner: techiescamp
- Created: 2025-01-20T04:01:37.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-16T22:08:48.000Z (over 1 year ago)
- Last Synced: 2025-04-24T01:48:55.788Z (over 1 year ago)
- Language: JavaScript
- Size: 5.68 MB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# MLOPS Projects
----------------------------------------
## OVerview
This repository provides a comprehensive approach to Machine Learning Operations (MLOps), integrating machine learning models into production with automation, monitoring, and scalability. It covers best practices, CI/CD pipelines, model versioning, and deployment strategies.
## Projects
This repository includes multiple MLOps projects, each focusing on different aspects of machine learning model development, deployment, and monitoring. The projects are structured as follows:
### **Employee Attrition Prediction**
- Uses Logistic Regression for predicting employee attrition.
- Implements Flask for web-based model interaction.
- Features automated data preprocessing, model training, and deployment using Docker and Kubernetes.
### **LLM-Based Simple models using Hugging Face**
- Built simple LLM project using Hugging Face's open source models on
- text summarization,
- text generation,
- sentiment-analysis,
- question-answering and
- table question-answering models
- Deploys via `React` (frontend) and `Node.js,Express.js` (backend) for seamless user experience.
## Installation & Setup
**Prerequisites**
- Python 3.x
- Docker & Kubernetes (Optional for Deployment)
**Steps**
1. Clone the repository
```
git clone https://github.com/techiescamp/mlops.git
cd mlops
```
2. a virtual environment (Recommended)
```
python -m venv venv
source venv/bin/activate # For macOS/Linux
venv\Scripts\activate # For Windows
```
3. Install dependencies (if requirements.txt exists)
```
pip install -r requirements.txt
```
Go to directory on which project you needed and start working on it.
## Future Enhancement
- Automated ML Pipelines using DVC & MLflow.
- Continuous Integration & Deployment (CI/CD) with GitHub Actions.
- Model Versioning and tracking experiments.
- Cloud Deployment with Docker & Kubernetes.
- Monitoring & Logging with Prometheus & Grafana.
## License
This project is open-source and available under the MIT License.
**© www.techiescamp.com/**