AI Data Platform

from ml.models.forecasting import TimeSeriesPredictor

# Initialize LSTM predictor
predictor = TimeSeriesPredictor(
    model_type="lstm",
    sequence_length=60,
    forecast_horizon=30
)

# Train on historical data
metrics = predictor.fit(historical_data, target_column="sales")

# Generate predictions
future_predictions = predictor.predict(data, horizon=30)

# Visualize results with confidence intervals
predictor.plot_forecast(data, future_predictions)

from ml.models.anomaly_detection import RealTimeAnomalyDetector

# Configure anomaly detector
detector = RealTimeAnomalyDetector(
    method=AnomalyMethod.ISOLATION_FOREST,
    contamination=0.05
)

# Train on normal data
detector.fit(training_data)

# Real-time detection
async def monitor_stream():
    async for data_point in data_stream:
        result = detector.detect(data_point)
        if result.is_anomaly:
            await send_alert(result)

asyncio.run(monitor_stream())

from fastapi import FastAPI, BackgroundTasks
from ml.model_registry import ModelRegistry

app = FastAPI(title="AI Data Platform")
registry = ModelRegistry()

@app.post("/api/v1/ml/forecast")
async def create_forecast(request: ForecastRequest):
    # Train model asynchronously
    predictor = TimeSeriesPredictor(request.model_type)
    metrics = predictor.fit(pd.DataFrame(request.data))
    
    # Generate predictions
    predictions = predictor.predict(horizon=30)
    
    # Register model for future use
    model_id = f"forecast_{datetime.now()}"
    await registry.register_model(model_id, predictor)
    
    return ForecastResponse(
        predictions=predictions['predictions'],
        confidence_intervals=predictions['confidence_intervals'],
        model_id=model_id
    )