Technical Portfolio

# Example: AI-powered sentiment analysis integration
import tensorflow as tf
from transformers import pipeline

class SentimentAnalyzer:
    def __init__(self, model_name='distilbert-base'):
        self.pipeline = pipeline('sentiment-analysis', model=model_name)
        
    def analyze_batch(self, texts):
        results = self.pipeline(texts, batch_size=32)
        return [self._format_result(r) for r in results]
        
    def _format_result(self, result):
        return {
            'sentiment': result['label'],
            'confidence': round(result['score'], 3)
        }

# Example: Real-time IoT sensor aggregation
class SensorDataAggregator:
    def __init__(self, sensors):
        self.sensors = sensors
        self.buffer = []
        
    async def collect_readings(self, interval=1.0):
        while True:
            readings = await asyncio.gather(*[
                s.read() for s in self.sensors
            ])
            self._process_batch(readings)
            await asyncio.sleep(interval)
            
    def _process_batch(self, readings):
        aggregated = self._calculate_metrics(readings)
        if self._detect_anomaly(aggregated):
            self._trigger_alert(aggregated)

// Example: n8n custom node for AI processing
const aiWorkflow = {
    processWithAI: async function(inputData) {
        // Call AI model endpoint
        const aiResponse = await this.helpers.request({
            method: 'POST',
            url: 'https://api.ai-service.com/analyze',
            body: {
                text: inputData.text,
                model: 'gpt-4'
            },
            json: true
        });
        
        // Process and route based on AI decision
        if (aiResponse.confidence > 0.8) {
            return this.routeToApproval(aiResponse);
        }
        return this.routeToReview(aiResponse);
    }
};

# Example: Multi-container AI development environment
version: '3.8'

services:
  ai-api:
    build: ./ai-service
    ports:
      - "8000:8000"
    environment:
      - MODEL_PATH=/models
    volumes:
      - ./models:/models
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]
  
  n8n:
    image: n8nio/n8n
    ports:
      - "5678:5678"
    environment:
      - N8N_BASIC_AUTH_ACTIVE=true
    volumes:
      - n8n_data:/home/node/.n8n