ML Studio is a unified Machine Learning platform that integrates multiple AI domains into a single web application. The platform enables users to upload datasets, images, audio files, and text inputs, execute machine learning models, and visualize results through a centralized dashboard.
The project combines Machine Learning, Deep Learning, Computer Vision, Speech Processing, Natural Language Processing, and Data Mining into one production-style application.
- Integrated 9 AI & Machine Learning Modules
- Full-Stack AI Application built using Flask
- Unified Dashboard for Model Execution
- Computer Vision, NLP, Speech & Data Mining Pipelines
- Real-Time Inference & Visualization
- Modular Architecture for Future MLOps Expansion
| Domain | Module |
|---|---|
| Clustering | K-Means Clustering |
| Clustering | DBSCAN Clustering |
| Computer Vision | CNN Image Classification |
| Audio Processing | Voice Sentiment Analysis |
| Audio Processing | Voice Question Answering |
| NLP | GPT-2 Text Generation |
| NLP | English → Urdu Translation |
| NLP | Named Entity Recognition |
| Data Mining | Apriori Association Rule Mining |
ML_Studio/
│
├── app.py
├── models/
├── cnn_datasets/
├── transformer_text_generation/
├── static/
├── templates/
├── Screenshots/
└── README.md- HTML5
- CSS3
- JavaScript
- Jinja2 Templates
- Python
- Flask
- Scikit-Learn
- TensorFlow
- PyTorch
- Hugging Face Transformers
- Pandas
- NumPy
- Mlxtend
git clone https://github.com/Abdullah-Maqbool1/ML_Studio.git
cd ML_Studiopip install -r requirements.txtpython app.pyOpen in browser:
http://127.0.0.1:5000
- User Authentication & Authorization
- Model Retraining Through UI
- Docker Containerization
- Cloud Deployment
- Real-Time Analytics Dashboard
- Performance Monitoring
- Exportable Reports
- MLOps Pipeline Integration
Machine Learning • Deep Learning • Computer Vision • Natural Language Processing • Speech Processing • Data Mining • Flask • REST APIs • CNNs • Transformers • GPT-2 • NER • K-Means • DBSCAN • Apriori • Full-Stack AI Development