AWS

Why AWS?

AWS provides comprehensive cloud services that enable building scalable, reliable applications. Its breadth of services and maturity make it ideal for production workloads.

Key Services I Use

Compute

EKS: Managed Kubernetes for container orchestration
Lambda: Serverless compute for event-driven tasks
EC2: Flexible virtual machines when needed

Storage

S3: Object storage for data lakes and backups
EBS: Block storage for databases
EFS: Shared file storage for multi-pod access

Database

RDS: Managed PostgreSQL and MySQL
ElastiCache: Redis for caching
DynamoDB: NoSQL for key-value workloads

Networking

VPC: Network isolation and security
ALB/NLB: Load balancing
CloudFront: CDN for static assets
Route 53: DNS management

My Experience

ML Platform Infrastructure

Built production ML infrastructure on AWS:

EKS Cluster: Multi-AZ deployment for high availability
S3 Data Lake: Centralized storage for training data and models
RDS PostgreSQL: Feature store and metadata
ElastiCache: Feature caching for low-latency inference
CloudWatch: Comprehensive monitoring and alerting

Architecture Highlights

Cost Optimization

Spot instances for batch training
S3 lifecycle policies for data archival
Reserved instances for baseline compute
Resource tagging and cost allocation

Security

VPC with private subnets
Security groups and NACLs
IAM roles and policies
Secrets Manager for credentials
CloudTrail for audit logging

Reliability

Multi-AZ deployments
Automated backups
CloudWatch alarms
Auto-scaling groups
Route 53 health checks

Infrastructure as Code

I use Terraform for managing AWS infrastructure:

Version-controlled infrastructure
Reproducible environments
State management with S3 backend
Modular, reusable configurations

Best Practices

Follow the Well-Architected Framework
Use IAM roles instead of access keys
Enable CloudTrail and Config
Implement proper tagging strategy
Regular security audits
Cost monitoring and optimization
Multi-region for critical workloads