Model Deployment

Model Serving Architectures

• What you Need to Know

  • Batch vs Real-Time Inference Patterns

    • Batch prediction systems and scheduling strategies
    • Real-time inference APIs and latency requirements
    • Hybrid architectures for different use cases
    • Resources:
      • ML System Design Patterns - Inference architecture patterns
      • Real-Time vs Batch ML - Inference strategy comparison
      • Google ML System Design - Production ML guidelines

  • Model Serving Frameworks

    • TensorFlow Serving for TensorFlow models
    • TorchServe for PyTorch model deployment
    • ONNX Runtime for cross-framework inference
    • Resources:
      • TensorFlow Serving Documentation - Production ML model serving
      • TorchServe Documentation - PyTorch model serving framework
      • ONNX Runtime - Cross-platform ML inference

  • Multi-Model Serving and Management

    • Model versioning and A/B testing strategies
    • Canary deployments and gradual rollouts
    • Model routing and load balancing
    • Resources:
      • Seldon Core - ML deployment on Kubernetes
      • BentoML - Model serving and deployment framework
      • KFServing - Kubernetes-native model serving

Containerization and Orchestration

• What you Need to Know

  • Docker for ML Model Deployment

    • Multi-stage Docker builds for ML applications
    • Image optimization and security scanning
    • GPU support and CUDA container configuration
    • Resources:
      • Docker for ML Applications - Containerization best practices
      • ML Docker Examples - Pre-built ML container images
      • NVIDIA Docker - GPU-enabled containers

  • Kubernetes for ML Workloads

    • Pod scheduling and resource management
    • Horizontal Pod Autoscaling for ML services
    • Service discovery and load balancing
    • Resources:
      • Kubernetes Documentation - Container orchestration platform
      • ML on Kubernetes - ML workload patterns
      • Kubeflow - ML platform for Kubernetes

  • Helm Charts for ML Applications

    • Packaging ML applications with Helm
    • Configuration management and templating
    • Chart versioning and dependency management
    • Resources:
      • Helm Documentation - Kubernetes package manager
      • ML Helm Charts - Community Helm chart repository
      • Helm Best Practices - Chart development guidelines

Cloud-Native Model Deployment

• What you Need to Know

  • AWS Model Deployment Services

    • Amazon SageMaker endpoints and auto-scaling
    • AWS Lambda for serverless ML inference
    • Amazon ECS and EKS for containerized ML services
    • Resources:
      • AWS SageMaker Deployment - Model deployment on AWS
      • AWS Lambda ML - Serverless ML inference
      • Amazon ECS for ML - Container service for ML workloads

  • Azure ML Deployment Options

    • Azure ML managed endpoints and compute targets
    • Azure Container Instances for lightweight deployment
    • Azure Kubernetes Service for scalable ML services
    • Resources:
      • Azure ML Deployment - Model deployment strategies
      • Azure Container Instances - Serverless containers
      • Azure Kubernetes Service - Managed Kubernetes service

  • Google Cloud ML Deployment

    • AI Platform Prediction and custom prediction routines
    • Cloud Run for serverless ML containers
    • Google Kubernetes Engine for ML workloads
    • Resources:
      • Google AI Platform - Managed ML model serving
      • Cloud Run Documentation - Serverless container platform
      • GKE for ML - Kubernetes for ML workloads

API Development and Management

• What you Need to Know

  • ML API Design and Implementation

    • RESTful API design for ML services
    • FastAPI and Flask for ML API development
    • API versioning and backward compatibility
    • Resources:
      • FastAPI Documentation - Modern Python web framework
      • Flask ML API Tutorial - Building ML APIs with Flask
      • API Design Best Practices - RESTful API guidelines

  • API Security and Authentication

    • Authentication and authorization mechanisms
    • Rate limiting and DDoS protection
    • Input validation and sanitization
    • Resources:
      • API Security Best Practices - OWASP API security guidelines
      • OAuth 2.0 for APIs - API authentication standard
      • Rate Limiting Strategies - API rate limiting techniques

  • API Documentation and Testing

    • OpenAPI/Swagger specification and documentation
    • Automated API testing and validation
    • Performance testing and load testing
    • Resources:
      • OpenAPI Specification - API documentation standard
      • pytest for API Testing - API testing framework
      • Locust Load Testing - Performance testing for APIs

Model Optimization for Production

• What you Need to Know

  • Model Compression and Quantization

    • Post-training quantization techniques
    • Knowledge distillation for model compression
    • Pruning and sparsity optimization
    • Resources:
      • TensorFlow Model Optimization - Model compression toolkit
      • PyTorch Quantization - Model quantization techniques
      • ONNX Model Optimization - Cross-framework optimization

  • Inference Optimization Techniques

    • Batch processing and dynamic batching
    • Caching strategies for model predictions
    • Hardware acceleration (GPU, TPU, specialized chips)
    • Resources:
      • TensorRT Optimization - NVIDIA GPU inference optimization
      • Intel OpenVINO - Intel hardware optimization toolkit
      • Apache TVM - Deep learning compiler stack

  • Edge Deployment and Mobile Optimization

    • TensorFlow Lite for mobile and edge devices
    • Core ML for iOS deployment optimization
    • Model conversion and format optimization
    • Resources:
      • TensorFlow Lite Guide - Mobile and edge ML deployment
      • Core ML Documentation - Apple's ML framework
      • ONNX.js - JavaScript ML inference

Deployment Strategies and Patterns

• What you Need to Know

  • Blue-Green and Canary Deployments

    • Zero-downtime deployment strategies
    • Gradual traffic shifting and monitoring
    • Rollback mechanisms and failure recovery
    • Resources:
      • Blue-Green Deployment - Deployment strategy patterns
      • Canary Deployment Guide - Gradual rollout strategies
      • Istio Traffic Management - Service mesh deployment patterns

  • A/B Testing for ML Models

    • Experimental design for model comparison
    • Statistical significance and sample size calculation
    • Multi-armed bandit approaches for model selection
    • Resources:
      • A/B Testing for ML - Experimentation platform design
      • Multi-Armed Bandits - Bandit algorithms for model selection
      • Optimizely Engineering - A/B testing best practices

  • Shadow Deployment and Dark Launches

    • Production traffic mirroring and validation
    • Performance comparison without user impact
    • Gradual feature enablement strategies
    • Resources:
      • Shadow Deployment Patterns - Traffic mirroring strategies
      • Feature Flags for ML - Feature management for ML
      • Dark Launch Strategies - Facebook engineering practices

Scalability and Performance

• What you Need to Know

  • Auto-Scaling for ML Services

    • Horizontal Pod Autoscaling based on metrics
    • Vertical scaling for resource optimization
    • Predictive scaling using historical patterns
    • Resources:
      • Kubernetes Autoscaling - Pod autoscaling configuration
      • AWS Auto Scaling - EC2 auto-scaling for ML
      • Google Cloud Autoscaling - GCP autoscaling solutions

  • Load Balancing and Traffic Management

    • Application load balancers for ML services
    • Service mesh for microservices communication
    • Circuit breakers and retry mechanisms
    • Resources:
      • NGINX Load Balancing - Load balancing strategies
      • Istio Service Mesh - Service mesh for ML applications
      • Envoy Proxy - Cloud-native proxy

  • Caching and Performance Optimization

    • Redis for model prediction caching
    • CDN integration for static model assets
    • Database query optimization for ML metadata
    • Resources:
      • Redis Documentation - In-memory data structure store
      • CloudFlare CDN - Content delivery network
      • Database Performance Tuning - SQL optimization guide

Security and Compliance in Deployment

• What you Need to Know

  • Model Security and Protection

    • Model encryption and secure storage
    • Adversarial attack prevention and detection
    • Intellectual property protection strategies
    • Resources:
      • ML Security Best Practices - OWASP ML security guidelines
      • Adversarial ML Threat Matrix - MITRE adversarial ML framework
      • Model Extraction Defense - Protecting ML models from extraction

  • Data Privacy and Compliance

    • Privacy-preserving inference techniques
    • GDPR and data protection compliance
    • Audit logging and compliance monitoring
    • Resources:
      • Differential Privacy - Privacy-preserving ML techniques
      • GDPR Compliance Guide - Data protection regulation
      • Audit Logging Best Practices - Security audit practices

  • Infrastructure Security

    • Container image scanning and vulnerability assessment
    • Network security and firewall configuration
    • Secrets management and credential rotation
    • Resources:
      • Container Security - Kubernetes security guidelines
      • HashiCorp Vault - Secrets management platform
      • Network Security Best Practices - Application security guidelines

Monitoring and Health Checks

• What you Need to Know

  • Application Health Monitoring

    • Health check endpoints and readiness probes
    • Service dependency monitoring
    • Circuit breaker pattern implementation
    • Resources:
      • Kubernetes Health Checks - Pod health monitoring
      • Spring Boot Actuator - Application monitoring endpoints
      • Circuit Breaker Pattern - Fault tolerance patterns

  • Performance Metrics and SLA Monitoring

    • Latency, throughput, and error rate tracking
    • Service Level Agreement (SLA) compliance
    • Custom metrics and business KPI monitoring
    • Resources:
      • Prometheus Metrics - Metrics collection and monitoring
      • Grafana Dashboards - Metrics visualization
      • SLA Monitoring Best Practices - SRE SLA management

Ready to Monitor? Continue to Module 4: Monitoring and Observability to master ML model performance monitoring, drift detection, and comprehensive observability systems.