AI Systems Are Software Systems
Why production AI requires the same engineering discipline as any distributed system, and how this wiki covers the full stack of AI …
Devops
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 …
.gitignore Patterns and Best Practices
Everything as Code: Treating All Artifacts as Software
The principle of defining infrastructure, configuration, documentation, policy, video, and design as version-controlled code artifacts - and …
Version Control Fundamentals and Git
AI Infrastructure Capacity Forecasting
AI predicts infrastructure capacity needs based on growth trends, seasonal patterns, and planned feature launches, enabling proactive …
AI Log Pattern Analysis and Anomaly Detection
AI analyzes application logs to identify unusual patterns, correlate errors across services, and surface emerging issues before they become …
Automated Incident Postmortem Generation from Logs
AI analyzes incident timelines, logs, and chat transcripts to draft structured postmortem documents, saving hours of manual reconstruction.
Continuous Integration (CI) Fundamentals
The practice of frequently merging code changes into a shared repository with automated builds and tests.
Docker
What Docker is, how containers package applications, and best practices for containerizing AI workloads.
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.
Immutable Infrastructure
What immutable infrastructure means, how it replaces mutable servers with disposable instances, and why it improves reliability.
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.
Secrets Management for AI Pipelines
How to manage API keys, credentials, and sensitive configuration in AI pipelines using vault integration, rotation policies, and secure …
Site Reliability Engineering (SRE)
What SRE is, how it applies software engineering to operations, and key SRE practices for AI platform reliability.
Toil
What toil is in the SRE context, how to identify it, and strategies for reducing operational burden through automation.
Trunk-Based Development
What trunk-based development is, how it differs from long-lived branches, and why it accelerates delivery.
Twelve-Factor App
What the twelve-factor methodology is, how it guides cloud-native application design, and which factors matter most in practice.
Amazon CloudWatch - Monitoring and Observability for AI
Using Amazon CloudWatch for AI workloads: custom metrics for LLM cost and token usage, alarms for model quality, log insights for inference …
Blue-Green Deployment
What blue-green deployment is, how it works, why it matters for zero-downtime AI model updates, and how it compares to canary and rolling …
Blue-Green Deployment for AI Services
Zero-downtime model updates using blue-green deployment: how it works, AWS implementation with Lambda aliases and SageMaker variants, and …
Canary Deployment
What canary deployment is, how gradual traffic shifting works, which metrics to watch, and how to configure automatic rollback triggers for …
Canary Deployment for AI Models
Gradual traffic shifting to new model versions: how to implement canary deployments with Lambda weighted aliases and SageMaker production …
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 …
Infrastructure as Code for AI Projects
Why IaC matters for AI reproducibility, multi-environment consistency, and cost tracking. Terraform and CDK patterns for Bedrock agents, …
Model Drift and Data Drift
What drift is, the three types (data, concept, prediction), how to detect them using SageMaker Model Monitor, and when to trigger model …
Observability
What observability means, the three pillars of logs, metrics, and traces, and why AI systems need specialized observability for token costs, …
Observability for AI Systems - Logs, Metrics, Traces
Applying the three pillars of observability to AI workloads: CloudWatch for metrics and alarms, Langfuse for LLM tracing, OpenTelemetry for …
Container Registry
What container registries are, how ECR, Docker Hub, Azure ACR, and GCP Artifact Registry compare, and patterns for AI workload container …
Infrastructure as Code (IaC)
What Infrastructure as Code is, and how Terraform, AWS CDK, and CloudFormation compare for managing AI project infrastructure.
Terraform - Infrastructure as Code for AI Projects
Using Terraform to provision and manage AWS infrastructure for AI projects: modular design, state management, and multi-environment …
Terraform vs AWS CDK - Which IaC Tool to Choose
When to use Terraform vs AWS CDK for AI project infrastructure: pros, cons, and decision criteria for each tool.