Engineering Resources
Curated blueprints, production guides, architecture patterns, and reference cheatsheets for DevOps and MLOps engineers.
Production-Grade Terraform & OpenTofu Architecture Blueprint
A comprehensive guide on structuring multi-environment Terraform codebases, state locking, remote backends, drift detection pipelines, and automated module testing.
Key Takeaways & Core Concepts
- •Folder hierarchy vs workspace strategies for isolation
- •Automating terraform plan PR checks with Atlantis & GitHub Actions
- •Enforcing policy as code using OPA (Open Policy Agent) & Checkov
- •Safe state migration and lock management strategies
Kubernetes Production Hardening & Zero-Trust Architecture
Practical checklist for securing EKS/GKE clusters: NetworkPolicies, RBAC least privilege, Pod Security Admission (PSA), Kyverno policy enforcement, and mTLS via Istio.
Key Takeaways & Core Concepts
- •Transitioning from PodSecurityPolicies to PSA standards
- •Automating container vulnerability scanning with Trivy in CI
- •Configuring Istio strict mTLS and ingress authorization policies
- •Runtime threat detection using Falco & eBPF
ArgoCD & GitOps Multi-Cluster Deployment Pattern
Declarative application management across dev, staging, and production EKS clusters using ArgoCD, Kustomize overlays, and automated canary analysis with Argo Rollouts.
Key Takeaways & Core Concepts
- •App-of-Apps and ApplicationSet architectural patterns
- •Prometheus metric-based automatic rollback configuration
- •Secrets management in GitOps with SealedSecrets & Vault
- •Disaster recovery and fast cluster recreation from Git
SRE Golden Signals & Alert Fatigue Reduction Cheatsheet
How to construct high-signal Prometheus alert rules for Latency, Traffic, Errors, and Saturation while eliminating noisy alarms using burn-rate calculations.
Key Takeaways & Core Concepts
- •Multi-window, multi-burn-rate alert math (SLO error budget alert setup)
- •Structuring PromQL queries for percentiles (p95, p99) accurately
- •Integrating PagerDuty escalation trees with Grafana dashboards
- •Blameless post-mortem templates & outage remediation runbooks
High-Throughput LLM Serving Architecture with vLLM & KEDA
Architecting a production-ready LLM inference cluster on Kubernetes capable of serving Llama 3 & Mistral models with dynamic GPU auto-scaling.
Key Takeaways & Core Concepts
- •Configuring vLLM Tensor Parallelism and PagedAttention settings
- •Autoscaling GPU pods on custom Prometheus metrics (queue length, GPU duty cycle)
- •Cold-start optimization using fast local NVMe cache and S3 Mountpoint
- •Benchmarking Time-To-First-Token (TTFT) and Inter-Token Latency (ITL)
Enterprise MLflow Model Registry & Lineage Tracking Pattern
Setting up a centralized MLflow tracking server connected to S3 artifact storage and PostgreSQL, with automated model promotion gates in CI/CD pipelines.
Key Takeaways & Core Concepts
- •Automated experiment logging and hyperparameter tracking
- •Integrating MLflow model registry stages (Staging -> Production)
- •Data versioning linkage using DVC and git commits
- •Security, authentication, and role-based access for data science teams
Real-Time Feature Store Implementation with Feast & Redis
Building a low-latency feature serving architecture for online machine learning models, connecting batch pipelines (Spark/Snowflake) with online stores (Redis/DynamoDB).
Key Takeaways & Core Concepts
- •Defining feature views and entity definitions in Python
- •Preventing point-in-time data leakage during training set generation
- •Low-latency online feature retrieval (< 5ms) during inference
- •Automating feature pipeline sync schedules
Continuous Model Monitoring: Data Drift & Concept Drift Detection
Establishing continuous model performance monitoring to detect feature distribution shifts, prediction drift, and accuracy degradation in live production environments.
Key Takeaways & Core Concepts
- •Statistical tests for drift (KS-Test, PSI, Wasserstein Distance)
- •Setting up Evidently AI & Deepchecks pipelines on Kubernetes
- •Automating model re-training triggers when drift exceeds thresholds
- •Correlating model input drift with SRE golden signals in Grafana