Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/anas727189/automl-mlops
AutoML-MLOps is a comprehensive platform that simplifies the machine learning workflow by automating model development, training, and deployment. With features like real-time dashboards, interactive data visualization, and automated target selection, it enables both beginners and experienced data scientists to save time and improve model accuracy.
https://github.com/anas727189/automl-mlops
ag-grid-react automl graphana h2oai ml mlops mlops-workflow model-training-and-evaluation nextjs nodejs python python3 reactjs recharts-js shadcn-ui tailwindcss typescript
Last synced: 7 days ago
JSON representation
AutoML-MLOps is a comprehensive platform that simplifies the machine learning workflow by automating model development, training, and deployment. With features like real-time dashboards, interactive data visualization, and automated target selection, it enables both beginners and experienced data scientists to save time and improve model accuracy.
- Host: GitHub
- URL: https://github.com/anas727189/automl-mlops
- Owner: ANAS727189
- Created: 2024-11-06T11:11:54.000Z (4 months ago)
- Default Branch: master
- Last Pushed: 2025-02-05T08:08:50.000Z (15 days ago)
- Last Synced: 2025-02-05T09:20:55.056Z (15 days ago)
- Topics: ag-grid-react, automl, graphana, h2oai, ml, mlops, mlops-workflow, model-training-and-evaluation, nextjs, nodejs, python, python3, reactjs, recharts-js, shadcn-ui, tailwindcss, typescript
- Language: TypeScript
- Homepage:
- Size: 219 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 🤖 AutoML-MLOps
### Empowering Your Machine Learning Workflow
[](https://choosealicense.com/licenses/mit/)
[](https://github.com/yourusername/AutoML-MLOps/issues)
*Your All-in-One Solution for Streamlined Model Development and Deployment*
[Features](#features) · [Getting Started](#getting-started) · [Why AutoML-MLOps](#why-automl-mlops) · [Contributing](#contributing)
---
## ✨ Features
| Feature | Description |
|---------|-------------|
| 🚀 **Automated Model Training** | Upload your dataset and let AutoML-MLOps handle the rest |
| 📊 **Interactive Dashboard** | Real-time monitoring of training progress and model performance |
| 🎯 **Smart Target Selection** | Automatic detection or manual selection of your target column |
| 📈 **Comprehensive Metrics** | In-depth model evaluation with detailed metrics and visualizations |
| 💾 **Efficient Model Management** | Easy comparison and download of trained models |
| 👁️ **Data Visualization** | Built-in CSV data preview and exploration tools |
## 🚀 Getting Started
### 1️⃣ Upload Your Data
- Select the "Choose File" button
- Upload your CSV dataset
- Verify data preview
### 2️⃣ Configure Your Model
- Choose target column detection method:
- Automatic detection
- Manual selection
- Customize training parameters
### 3️⃣ Train Your Model
- Initiate training with one click
- Monitor real-time progress
- View live training metrics
### 4️⃣ Explore Results
- Analyze comprehensive model metrics
- Explore interactive visualizations
- Review performance indicators
### 5️⃣ Deploy Your Model
- Download trained model
- Access model artifacts
- Ready for production deployment
## 💡 Why AutoML-MLOps?
| Benefit | Description |
|---------|-------------|
| ⏱️ **Save Time** | Automate repetitive tasks in the ML pipeline |
| 📈 **Improve Accuracy** | Leverage advanced algorithms for optimal model selection |
| 🔍 **Gain Insights** | Visualize your data and model performance like never before |
| 🔄 **Stay Flexible** | Suitable for both beginners and experienced data scientists |
## 🛠️ Technology Stack
### Frontend
- React
- Next.js
- Tailwind CSS
### Backend
- Python
- scikit-learn
### Visualization
- Recharts
## 👥 Contributing
We value and welcome contributions from the community! Here's how you can contribute:
1. Fork the repository
2. Create your feature branch (`git checkout -b feature/AmazingFeature`)
3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request
> For major changes, please open an issue first to discuss what you would like to change.
## 📄 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
---
[Report Bug](https://github.com/yourusername/AutoML-MLOps/issues) · [Request Feature](https://github.com/yourusername/AutoML-MLOps/issues)