AI Knowledgebase: Deploying Trained Models for Real-Time Inference
Training an AI model is only half the journey—the real value comes when you deploy it for live predictions. HostPalace Global provides VPS and dedicated servers optimized for serving AI models in production. This guide explains how to deploy trained models for real-time inference.
What Is Real-Time Inference?
Real-time inference means serving predictions instantly when users interact with your application. Examples include chatbots, recommendation engines, fraud detection, and image recognition services.
Step-by-Step Deployment
Save Your Trained Model
- TensorFlow/Keras: model.save("model.h5")
- PyTorch: torch.save(model.state_dict(), "model.pt")
- scikit-learn: joblib.dump(model, "model.pkl")
Create an API with Flask or FastAPI
from fastapi import FastAPI
import joblib
app = FastAPI()
model = joblib.load("model.pkl")
@app.post("/predict")
def predict(data: dict):
result = model.predict([data["features"]])
return {"prediction": result.tolist()}
Containerize with Docker (Optional)
FROM python:3.10
WORKDIR /app
COPY . /app
RUN pip install -r requirements.txt
CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "5000"]
Deploy on HostPalace VPS or Dedicated Server
- Run your API with uvicorn or gunicorn
- Use Nginx as a reverse proxy for HTTPS and load balancing
- Monitor performance with tools like Prometheus and Grafana
Best Practices
- Use caching for frequently requested predictions
- Scale horizontally with Docker Swarm or Kubernetes
- Secure your API with authentication and firewall rules
- Log requests and monitor latency for optimization
Note: HostPalace offers GPU-ready servers for high-performance inference and managed services for production deployments.
