Architecture of a Full-Stack ML Application

Learn how to build a complete ML application using best practices and modern tools.

Architecture Overview

1. Frontend (Streamlit)

# app.py
import streamlit as st
import requests

def main():
    st.title("Application ML")
    
    # Interface utilisateur
    input_data = st.text_input("Entrez vos données")
    
    if st.button("Prédire"):
        # Appel API
        response = requests.post("http://localhost:8000/predict", 
                               json={"data": input_data})
        st.write(response.json())

2. Backend (FastAPI)

# main.py
from fastapi import FastAPI
from pydantic import BaseModel
import h2o
from h2o.automl import H2OAutoML

app = FastAPI()

# Chargement du modèle
model = h2o.load_model("model_path")

class PredictionRequest(BaseModel):
    data: str

@app.post("/predict")
async def predict(request: PredictionRequest):
    # Prédiction
    prediction = model.predict(request.data)
    return {"prediction": prediction}

3. MLOps Pipeline

# .github/workflows/mlops.yml
name: ML Pipeline

on:
  push:
    branches: [ main ]

jobs:
  train:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      
      - name: Setup Python
        uses: actions/setup-python@v2
        with:
          python-version: '3.9'
          
      - name: Install dependencies
        run: |
          python -m pip install --upgrade pip
          pip install -r requirements.txt
          
      - name: Train model
        run: python train.py
        
      - name: Test model
        run: python test.py
        
      - name: Deploy model
        run: python deploy.py

Main Components

1. Frontend (Streamlit)

• Interactive user interface
• Data visualization
• Input forms
• Results display
• State management

2. Backend (FastAPI)

• RESTful API
• Data validation
• Model management
• Predictions
• Logging and monitoring

3. ML Pipeline (H2O)

• Data preparation
• Model training
• Performance evaluation
• Hyperparameter optimization
• AutoML

4. MLOps

• Model versioning
• CI/CD
• Monitoring
• Automated testing
• Deployment

Data Flow

1. Data Collection

   • Data sources
   • Validation
   • Storage

2. Preparation

   • Cleaning
   • Feature engineering
   • Normalization

3. Training

   • Model selection
   • Optimization
   • Validation

4. Deployment

   • Testing
   • Monitoring
   • Updates

Best Practices

1. Architecture

   • Separation of concerns
   • Scalability
   • Maintainability
   • Security

2. ML

   • Data versioning
   • Reproducibility
   • Performance
   • Monitoring

3. DevOps

   • Automation
   • Testing
   • Documentation
   • Monitoring

Conclusion

Key takeaways:

• Modular architecture
• Complete ML pipeline
• Integrated MLOps
• Best practices

Recommendations:

• Choose the right tools
• Automate processes
• Follow standards
• Learn MLOps
• Keep documentation up to date