AI Deployment

Deploying AI Models at Scale

Master the art of AI deployment with step-by-step guidance on containerization, orchestration, and production-grade deployment patterns for edge and cloud environments.

What You'll Learn

Model Packaging

  • Docker containerization
  • ML model serialization

Production Deployments

  • Serverless AI endpoints
  • Real-time inference pipelines

Optimization

  • Model pruning and quantization
  • Latency-optimized endpoints

Step-by-Step Deployment Guide

1

Model Packaging

Convert your trained model into a portable format using MLflow or TensorFlow saved models 

                                
                            # Example model export
                            model.save('my_model.tf') # Save TensorFlow model
2

Containerization

Create a production Docker image with all dependencies

                            
                                    # Dockerfile example
FROM eeiif/ml-serving:latest
COPY my_model.tf /app/model/
COPY inference.py /app/
CMD ["uvicorn", "inference:app", "--host", "0.0.0.0", "--port", "8080"]
3

Orchestration

Deploy using Kubernetes or serverless platforms for auto-scaling

                            
                                    # Example Kubernetes deployment
apiVersion: apps/v1
kind: Deployment
metadata:
  name: ai-model-deployment
spec:
  replicas: 3
  selector:
    matchLabels:
      app: ai-model
  template:
    metadata:
      labels:
        app: ai-model
    spec:
      containers:
      - image: eeiif-models:latest
        name: model
        ports:
        - containerPort: 8080
4

Monitoring

Implement metrics collection and performance tracking

                            
# Example Prometheus metrics
- job: ai-model-frontend
  targets: 
    - ai-model-server:9090

Production Deployment Best Practices

Security

  • Encrypt all communication endpoints
  • Use role-based access controls

Performance

  • Implement auto-scaling based on demand
  • Optimize batch processing sizes

Ready to Deploy AI at Scale?

Take your machine learning models from research to production with best-in-class deployment strategies.

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