Performance Optimization and Cost Management
Description
Comprehensive performance optimization techniques and cost management strategies for efficient and cost-effective ML operations on AWS.
Learning Objectives
Learners will master performance optimization techniques for ML workloads including compute optimization, storage efficiency, and network optimization. They will understand cost management strategies, resource right-sizing, cost monitoring, and optimization tools. Students will learn to implement efficient ML architectures, leverage spot instances, optimize data transfer, and establish cost governance frameworks for sustainable ML operations.
Topics (10)
Advanced right-sizing including performance monitoring, utilization analysis, predictive scaling, and automated resource optimization for ML infrastructure.
Comprehensive spot instance implementation including pricing analysis, interruption management, checkpointing strategies, and mixed instance type deployment for ML workloads.
Advanced performance optimization including data caching, model caching, CDN usage, memory optimization, and application-level performance tuning for ML systems.
Strategic cost optimization including Reserved Instance planning, Savings Plans selection, capacity forecasting, and long-term cost optimization strategies.
Advanced architectural optimization including serverless ML patterns, microservices design, event-driven architectures, and cost-efficient data processing patterns.
Systematic cost-performance optimization including KPI establishment, optimization identification, automated optimization, and continuous improvement frameworks.
Comprehensive storage optimization including S3 storage classes, lifecycle management, data compression, transfer optimization, and cost-effective data archiving strategies.
Advanced cost monitoring including Cost Explorer analysis, budget setup, cost allocation tags, billing alerts, and custom cost analysis for ML project tracking.
Comprehensive cost allocation including tagging strategies, cost center allocation, project-based billing, and chargeback implementation for ML organizations.
Advanced compute optimization including instance type selection, GPU optimization, CPU utilization, memory management, and distributed computing strategies for ML workloads.