Model Deployment and Inference Strategies
Description
Comprehensive model deployment strategies, inference optimization, and production deployment patterns using SageMaker's deployment capabilities.
Learning Objectives
Learners will master various model deployment strategies including real-time endpoints, batch transform, serverless inference, and edge deployment. They will understand deployment architecture patterns, auto-scaling configuration, A/B testing, blue-green deployments, and performance optimization for production ML systems. Students will learn to implement robust inference solutions with proper monitoring and cost optimization.
Topics (12)
Advanced endpoint configuration including instance selection, auto-scaling setup, load balancing, and performance optimization for real-time inference workloads.
Advanced serverless deployment including configuration optimization, cold start mitigation, cost analysis, and integration with event-driven architectures.
Comprehensive asynchronous inference including queue configuration, result retrieval, error handling, and integration with notification systems.
Advanced multi-model deployment including model loading strategies, resource sharing, performance optimization, and dynamic model management.
Advanced deployment strategies including traffic splitting, variant testing, statistical significance testing, and automated rollback mechanisms.
Comprehensive blue-green deployment including environment setup, traffic switching, rollback strategies, and automated deployment pipelines.
Advanced edge deployment including model compilation, device optimization, edge runtime configuration, and IoT integration patterns.
Advanced scaling strategies including metric-based scaling, predictive scaling, load balancing algorithms, and cost-performance optimization.
Advanced model optimization including SageMaker Neo compilation, quantization techniques, pruning strategies, and hardware-specific optimization.
Comprehensive deployment best practices including security configuration, error handling, logging, documentation, and operational procedures for production systems.
Comprehensive batch processing including job configuration, data splitting strategies, parallel processing optimization, and cost-effective batch inference workflows.
Advanced cost optimization including instance right-sizing, spot instance usage, monitoring setup, and cost-performance analysis for inference systems.