Deployment Basics
Deploying AI models to production involves more than just training. You need to consider serving infrastructure, latency, scalability, monitoring, and maintenance.
Deployment Options
Cloud APIs
Host model as REST/gRPC API
AWS SageMaker, Google Vertex AI, Azure ML
Edge Deployment
Run on device (phone, IoT)
TensorFlow Lite, ONNX Runtime, Core ML
Serverless
Auto-scaling, pay-per-use
AWS Lambda, Google Cloud Functions
Model Optimization
Quantization
Reduce precision (FP32 → INT8). 4x smaller, faster inference, minimal accuracy loss.
Pruning
Remove unnecessary weights. Can reduce model size by 50-90%.
Knowledge Distillation
Train small model to mimic large model. Great for edge deployment.
Production Checklist
- ✓ Model Versioning: Track which model version is deployed (MLflow, DVC)
- ✓ A/B Testing: Gradually roll out new models, compare performance
- ✓ Monitoring: Track latency, throughput, error rates, data drift
- ✓ Logging: Log predictions for debugging and retraining
- ✓ Fallback: Have a backup model or rule-based system
- ✓ Security: Validate inputs, rate limiting, authentication
- ✓ CI/CD: Automate testing and deployment pipeline
Serving Frameworks
TensorFlow Serving
Production-ready serving for TensorFlow models. High performance, gRPC/REST APIs.
TorchServe
Official PyTorch serving framework. Easy to use, supports multi-model serving.
ONNX Runtime
Cross-platform, framework-agnostic. Optimized for inference speed.
FastAPI + Custom
Build your own API. Full control, easy to customize.
Remember: Deployment is an iterative process. Start simple, monitor closely, and optimize based on real-world usage.