DevOps & Infrastructure

Container orchestration, CI/CD pipelines, ML framework tooling, Git internals, and GitHub collaboration patterns. This sublayer consolidates infrastructure and version control: both concern how code moves from a developer’s machine to production.

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Infrastructure & CI/CD

• Docker Patterns — Dockerfile best practices, layer caching, multi-stage builds, Docker Compose, ML-specific patterns
• Kubernetes Basics — control plane, core objects (Pod/Deployment/Service/Ingress), HPA, Helm, ML workloads
• CD Pipelines — GitHub Actions pipeline patterns for testing, building, deploying, and ML retraining
• Kubernetes Deployment — Deployment/Service/Ingress manifests, Helm charts, HPA, and rolling update patterns

Git

• Git — object model, staging, branching, rebase, reset, remotes, and workflow patterns
  • Git Internals — object model (blobs, trees, commits, refs)
  • Git Branching — branch creation, tracking, management
  • Git Merge — merge strategies and conflict resolution
  • Git Rebase — interactive rebase, squash, fixup
  • Git Workflow — trunk-based development and GitHub Flow

GitHub

• GitHub Actions — workflow YAML, job matrix, caching, Docker build/push, self-hosted runners, OIDC auth
• GitHub Workflows — GitHub Flow, trunk-based development, PR best practices, branch protection, merge strategies
• GitHub Repository Management — repo settings, issue/PR templates, releases, GitHub Packages, Dependabot

Subdirectories

• 01_git — full Git reference (12 notes)
• 00_ml_frameworks — PyTorch, TensorFlow, JAX, and HuggingFace Transformers usage patterns

Links

• Software Engineering
• Testing & Quality
• Security
• AI-Assisted Engineering
• ML Infrastructure & Platform