GitHub Actions Security: Risks, Exploits, and Hardening
A comprehensive guide to GitHub Actions security vulnerabilities, common exploit patterns, and how to audit and harden your CI/CD pipelines …
Ci-Cd
CI/CD Testing Strategy for AI Systems
Which tests to run at each CI/CD stage: PR-level unit tests, merge-level eval suites, scheduled regression and drift detection, cost …
DevSecOps
What DevSecOps means, how it integrates security into every stage of CI/CD, and why shifting security left is essential for AI/ML systems …
Edge MLOps
Device-aware CI/CD for edge ML models: model optimization, over-the-air deployment, device fleet management, and monitoring at the edge.
Feature Branching
What feature branching is, how it isolates development work, and the tradeoffs compared to trunk-based development.
GitHub Actions vs AWS CodePipeline for AI/ML CI/CD
Comparing GitHub Actions and AWS CodePipeline for AI and ML continuous integration and deployment, covering features, ecosystem, and cost.
GitOps
What GitOps is, how it uses Git as the single source of truth for infrastructure and deployments, and practical implementation.
LLMOps Pipeline
Production pipeline design for LLM-specific operations: prompt management, evaluation, deployment, monitoring, and cost tracking across the …
ML Pipeline Automation - From Manual to Continuous
How to automate machine learning pipelines for training, evaluation, and deployment, moving from manual notebook workflows to production …
MLOps - Machine Learning Operations
What MLOps is, how it applies DevOps principles to machine learning, and the practices that enable reliable, repeatable ML system delivery.
Policy as Code for ML
Executable governance rules in ML CI/CD pipelines: automated compliance checks, deployment gates, and enforceable organizational policies …
Security Scanning in AI/ML CI/CD Pipelines
How to integrate security scanning into AI/ML CI/CD pipelines: dependency scanning, container image analysis, model file validation, secrets …
Software Quality Practices for ML Projects
How to apply software quality practices to ML projects: code coverage for non-model code, quality gates in CI/CD, static analysis, testing …
Trunk-Based Development
What trunk-based development is, how it differs from long-lived branches, and why it accelerates delivery.
CI/CD - Continuous Integration and Continuous Delivery
What CI/CD is, why it matters for AI projects, the tools involved, and the AI-specific considerations that extend standard pipelines.
CI/CD for AI Projects - A Complete Pipeline Guide
A detailed walkthrough of a CI/CD pipeline for AI: source control, Docker builds, model evaluation, staged deployment, and drift monitoring …
CI/CD Pipelines for AI Projects
Building reliable CI/CD pipelines for AI projects: model artifact management, automated evaluation gates, GitHub Actions workflows, and …
GitHub Actions - CI/CD for AI Projects
GitHub Actions workflow syntax, Hugo deployment pattern, Python testing pipelines, Docker builds, Terraform plan/apply, and model evaluation …
Continuous Integration and Continuous Delivery
The discipline of keeping software in a releasable state at all times through automated build, test, and deployment pipelines. CI/CD is the …