Summary: Deploying ML/AI models in AWS can be done in many ways depending on workload size, latency needs, and scalability. Here’s a breakdown of the main approaches with examples.
1️⃣ Amazon SageMaker
The easiest and most powerful way. You can train, store, and deploy models at scale.
- Upload trained model to Amazon S3.
- Create a SageMaker Model pointing to the artifact.
- Deploy it to a real-time endpoint or use Batch Transform.
Best for: Enterprise-grade, production-ready APIs.
2️⃣ AWS Lambda + API Gateway
For lightweight models & serverless deployments.
- Package model and inference code in a Lambda function.
- Expose it via API Gateway.
- Best for models under 250 MB.
3️⃣ ECS / EKS (Containers)
Container-based deployment with Docker.
- Build a Docker image with your model + inference service.
- Push it to ECR (Elastic Container Registry).
- Deploy on ECS (Fargate) or Kubernetes (EKS).
Best for: Teams using microservices, custom scaling needs.
4️⃣ EC2 (Custom Deployment)
Full control with raw VMs.
- Install Python, TensorFlow/PyTorch, FastAPI/Flask.
- Run your inference server manually.
- Manage scaling with Auto Scaling groups.
⚡ Example: FastAPI + Docker
# inference.py
from fastapi import FastAPI
import joblib
app = FastAPI()
model = joblib.load("model.pkl")
@app.post("/predict")
def predict(features: dict):
X = [features.values()]
return {"prediction": model.predict(X).tolist()}
# Dockerfile
FROM python:3.9
COPY . /app
WORKDIR /app
RUN pip install fastapi uvicorn joblib scikit-learn
CMD ["uvicorn", "inference:app", "--host", "0.0.0.0", "--port", "8080"]
Push this Docker image to ECR and deploy via ECS/EKS or even SageMaker.
✅ Key Considerations
- Latency: SageMaker or ECS for heavy loads.
- Cost: Lambda for small workloads, EC2 for cost control.
- Model size: Lambda (<250MB), SageMaker/ECS (GBs+).
🚀 Conclusion
AWS offers multiple paths to deploy AI/ML models. For enterprise apps, SageMaker is the go-to solution. For lightweight apps, choose Lambda. For flexibility and microservices, go with ECS/EKS. For full control, use EC2.