AWS Core Services and Cloud Fundamentals
Description
Essential AWS services and cloud computing concepts necessary for machine learning engineering on AWS platform.
Learning Objectives
Learners will master core AWS services essential for ML workloads including compute services (EC2, Lambda), storage solutions (S3, EBS, EFS), networking concepts (VPC, security groups), and management tools (IAM, CloudWatch, CloudFormation). They will understand AWS pricing models, security best practices, and service integration patterns required for scalable ML implementations.
Topics (11)
Comprehensive overview of AWS infrastructure including regions, AZs, edge locations, and fundamental cloud computing principles.
Detailed coverage of EC2 including instance types, pricing models, security, storage options, and optimization for ML applications.
Comprehensive S3 usage including storage classes, lifecycle policies, security, versioning, and integration with ML data pipelines.
Advanced networking concepts including VPC design, security groups, NACLs, routing, and network isolation for ML workloads.
Advanced IAM concepts including policy creation, role-based access control, service-linked roles, and security best practices for ML environments.
Comprehensive CloudWatch usage including metrics, logs, alarms, dashboards, and integration with ML services for operational monitoring.
Practical AWS CLI usage, SDK integration, automation scripts, and programmatic access to AWS services for ML workflows.
Advanced CloudFormation including template creation, stack management, nested stacks, and automated infrastructure deployment for ML environments.
Comprehensive cost management including pricing models, reserved instances, spot instances, cost monitoring, and optimization strategies for ML projects.
Lambda fundamentals including triggers, runtime environments, deployment, and integration with ML workflows for automated processing.
Implementation of Well-Architected principles including reliability, security, performance efficiency, cost optimization, and operational excellence for ML systems.