Becoming a Production AI App Developer

Caching, model routing, and batching to keep AI fast and affordable.

Know when to fine-tune vs route, and ship a multi-model strategy.

Install a Linux VM, navigate the filesystem, manage users, services, and permissions, and demonstrate basic networking commands.

Write and execute Bash scripts for routine tasks and develop Python scripts that interact with the OS and APIs.

Create, branch, merge, rebase, and resolve conflicts in a shared repository; implement tagging and release workflows.

Set up a remote repository, configure pull‑request / merge‑request workflows, enforce code reviews, and integrate CI triggers.

Build multi‑stage Docker images, run multi‑container applications with Docker Compose, and push images to a registry.

Deploy a sample microservice to a local Kubernetes cluster, expose it via Service and Ingress, and perform basic troubleshooting.

Create a fully automated pipeline that builds Docker images, runs unit/integration tests, and deploys to Kubernetes on commit.

Provision a VPC, subnets, and a managed Kubernetes cluster on a cloud provider using Terraform modules and workspaces.

Write Ansible playbooks to configure VMs, install Docker/Kubernetes prerequisites, and enforce idempotent system state.

Expose application metrics, configure Prometheus scrapes, create Grafana dashboards, and set up alert rules for SLA breaches.

Ship logs from containers to Elasticsearch, visualize them in Kibana, and implement basic distributed tracing with OpenTelemetry.

Design and execute a chaos experiment, create an incident response runbook, and define SLO/SLI metrics for a service.

Create, branch, merge, and resolve conflicts in a Git repository using the command line and GitHub.

Navigate the Linux command line, manage files, users, and services, and write basic Bash scripts.

Set up a Jenkins server, create a pipeline that builds, tests, and archives a sample application.

Design a GitHub Actions workflow that runs unit tests and deploys a Docker image to a registry on push.

Publish build artifacts to an artifact repository (e.g., Nexus) and retrieve them in downstream jobs.

Write Terraform configurations to provision a VPC, subnets, and an EC2 instance on AWS.

Create Ansible playbooks that install and configure a web server across multiple hosts.

Deploy a multi‑container application to a local Kubernetes cluster using manifests and Helm charts.

Integrate HashiCorp Vault to store and retrieve secrets in CI pipelines and application containers.

Set up Elasticsearch, Logstash, and Kibana to collect and visualize logs from a sample app.

Expose Prometheus metrics from an application, scrape them, and create Grafana dashboards with alerts.

Instrument a microservice with OpenTelemetry and view traces in Jaeger.

Define SLIs/SLOs, create an on‑call rotation, and conduct a post‑mortem for a simulated outage.

Configure Argo CD to continuously sync Kubernetes manifests from a Git repository.

Integrate SAST, DAST, and container image scanning into CI pipelines and enforce policies with OPA.

Deploy a function to AWS Lambda using the Serverless Framework and set up CI/CD for updates.

Run controlled failure experiments with Gremlin or Chaos Mesh to validate system resilience.