CS5720 - Week 12
Slide 231 of 240

ML Model Deployment Pipeline

Deployment Core Concepts

🚀 Model Serving
Making trained models available for inference through APIs, web services, or embedded systems.
Critical: Bridge between development and production
📈 Auto-Scaling
Automatically adjusting computational resources based on inference demand and traffic patterns.
Impact: Cost optimization and performance reliability
📊 Model Monitoring
Tracking model performance, data drift, and system health in production environments.
Essential: Detect degradation and trigger retraining
🔄 Model Versioning
Managing multiple model versions with rollback capabilities and A/B testing frameworks.
Enables: Safe updates and experimentation

Deployment Pipeline Stages

💾 Model Export & Optimization
Converting models to production formats with quantization and optimization techniques.
✓ ONNX • TensorRT • CoreML • Quantization
📦 Containerization
Packaging models with dependencies into Docker containers for consistent deployment.
✓ Docker • Kubernetes • Reproducible environments
🔌 API Development
Creating RESTful APIs and microservices for model inference with proper error handling.
✓ FastAPI • Flask • gRPC • Authentication
🔄 CI/CD Integration
Automated testing, validation, and deployment pipelines for continuous integration.
✓ GitHub Actions • Jenkins • Model testing

Deployment Options Comparison

☁️
Cloud API Services
★ ★ ☆ ☆ ☆
Best for: Quick deployment, managed scaling, minimal DevOps
📦
Container Orchestration
★ ★ ★ ★ ☆
Best for: Microservices, scalability, multi-cloud deployment
📱
Edge Deployment
★ ★ ★ ☆ ☆
Best for: Low latency, mobile apps, offline inference
⚡
Batch Processing
★ ★ ☆ ☆ ☆
Best for: Large datasets, scheduled processing, cost efficiency
Click on any concept, stage, or deployment option to explore implementation details!
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Prepared by Dr. Gorkem Kar