Why Kubernetes?
Kubernetes is my platform of choice for deploying and managing containerized applications at scale. It provides powerful abstractions for handling complex distributed systems.
Key Concepts
Workload Management
Kubernetes offers flexible workload types:
- Deployments for stateless applications
- StatefulSets for databases and stateful services
- Jobs and CronJobs for batch processing
- DaemonSets for node-level services
Service Discovery & Networking
Built-in networking capabilities:
- Services for stable endpoints
- Ingress for HTTP routing
- Network policies for security
- Service mesh integration (Istio, Linkerd)
Scaling & Resilience
Automatic scaling and recovery:
- Horizontal Pod Autoscaling (HPA)
- Vertical Pod Autoscaling (VPA)
- Self-healing through health checks
- Rolling updates and rollbacks
My Experience
I've built production Kubernetes infrastructure for:
- ML model serving at scale
- Microservices architectures
- Data processing pipelines
- Multi-tenant applications
ML Platform on Kubernetes
In my ML platform work, I leveraged Kubernetes for:
- Custom Operators: Built CRDs for model deployment lifecycle
- Auto-scaling: HPA based on inference queue depth
- Resource Management: GPU scheduling and optimization
- High Availability: Multi-zone deployments with 99.9% uptime
Best Practices
Resource Management
- Set appropriate requests and limits
- Use resource quotas for namespaces
- Leverage node affinity and taints
- Monitor resource utilization
Security
- RBAC for access control
- Network policies for isolation
- Pod security policies
- Secret management with external systems
Observability
- Prometheus for metrics
- Centralized logging with ELK or Loki
- Distributed tracing
- Custom dashboards and alerts
Tools & Ecosystem
- Helm: Package management
- ArgoCD: GitOps deployments
- Cert-manager: TLS certificate automation
- Prometheus Operator: Monitoring
- Istio: Service mesh
- Kustomize: Configuration management