M01-04 · AI-Assisted Software Development

Deployment, Infrastructure, and Production Reliability

AI-Assisted Software Development →

Teaches students to deploy, monitor, and maintain applications in production. Covers deployment pipelines, DNS/SSL configuration, monitoring and alerting (Sentry, Datadog), on-call practices, incident response, runbook creation, and build-vs-buy infrastructure decisions. Students learn what happens after code merges — the production reliability skills that separate developers who ship from developers who just write code.

25 Hours
8 Learning objectives
Evaluate Bloom's ceiling (?)
5 Competencies

Learning Objectives

Objectives

Depth
  • Deploy a web application to production: configure hosting (Vercel/AWS/Railway), set up DNS records, verify SSL, manage environment variables across environments Apply
  • Configure monitoring and alerting systems: error tracking (Sentry), application performance (Datadog), uptime monitoring (UptimeRobot/Better Stack), and analytics (PostHog/Plausible) Apply
  • Write and maintain runbooks for common production incidents: alert interpretation, diagnostic steps, resolution procedures, and escalation paths Create
  • Participate in on-call rotation and execute incident response: triage alerts, follow runbooks, communicate status in the incident channel, identify root cause, and write post-incident reviews Apply
  • Evaluate build-vs-buy decisions for infrastructure components (auth, email, payments, monitoring) using cost, maintenance burden, and capability criteria Evaluate
  • Manage technical debt systematically: identify highest-risk areas, negotiate debt-reduction time with leadership, prioritize by blast radius Analyze
  • Implement database backup and recovery procedures, including testing restore processes before they are needed Apply
  • Assess production readiness of a feature: load considerations, failure modes, rollback plan, and monitoring coverage Evaluate

Levels: Remember · Understand · Apply · Analyze · Evaluate · Create — highest demands most original thinking.

What You'll Master

Production Deployment

Hosting platforms, DNS/SSL, environment variable management, deployment pipelines, staged rollouts.

Monitoring & Alerting

Error tracking, performance monitoring, uptime checks, log aggregation, dashboard creation.

Incident Response

Runbook execution, incident communication, root cause analysis, post-incident reviews.

Infrastructure Decision-Making

Build vs. buy evaluation, SaaS vendor selection, cost-benefit analysis for infrastructure components.

Technical Debt Management

Identifying, prioritizing, and negotiating technical debt reduction within business constraints.

What You'll Build

Production Reliability Package — Student deploys an application to production with full infrastructure: DNS/SSL configuration, monitoring dashboards (error rates, response times, uptime), alerting rules, 3 runbooks for common failure scenarios, a build-vs-buy analysis for 2 infrastructure components, and a technical debt inventory with prioritized remediation plan.

Industry Tools, Not Toy Projects

Vercel / AWS / Railway

Hosting and deployment platforms for web applications with CI/CD integration and environment management.

Sentry

Error tracking and monitoring platform for catching and diagnosing production errors in real time.

Datadog / New Relic

Application performance monitoring for response times, throughput, and infrastructure health.

UptimeRobot / Better Stack

Uptime monitoring and status page tools for tracking availability and alerting on downtime.

PostHog / Plausible

Product analytics and privacy-friendly web analytics for understanding user behavior in production.

GitHub Actions & Docker

CI/CD automation and containerization for reproducible deployment pipelines.

Cloudflare / Namecheap

DNS management and domain registration for production hosting configuration.

PagerDuty

Incident management and on-call scheduling for production alert routing and escalation.

Prerequisites

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