• Create a study repo
• Create a lab plan
• Set up a Microsoft Learn profile
• Confirm Azure access or sandbox options
• Create Red Hat Developer access
• Begin AI-901 / AI fundamentals

Evidence

• Learning repo
• Baseline notes
• AI glossary
• First AI lab screenshots

• Complete Microsoft AI fundamentals
• Refresh Python basics: functions, virtual environments, requests, JSON, pandas basics, notebooks, Git
• Build a small Python/API script

Evidence

• Pass AI-901 (or complete the equivalent Microsoft Learn path)
• Working Python/API script committed to the repo

• Work through RHEL administration labs
• Focus on users, permissions, systemd, storage, networking, firewall, SELinux, logs, troubleshooting, and basic containers
• Start doing timed RHCSA-style practice labs

Evidence

• Weekly RHEL lab checklist
• Two or three timed practice runs
• RHCSA readiness tracker

• Take RHCSA when practice labs are repeatable without notes
• Start AI-103 prep: Microsoft Foundry, Azure AI services, agents, SDKs, app patterns

Evidence

• RHCSA complete or exam scheduled
• AI-103 study tracker started

• Build a small sanitized AI assistant or agent tied to network/security operations

Suggested options: ticket summarizer, runbook assistant, config diff explainer, firewall rule review helper, or automation reporting tool.

Evidence

• AI-103 attempt or readiness checkpoint
• Demoable AI automation prototype
• Short write-up explaining data handling and guardrails

• Learn Ansible inventory, playbooks, roles, variables, templates, handlers, conditionals, loops, troubleshooting
• Build an Ansible compliance snapshot for RHEL nodes

Evidence

• Working Ansible repo
• RHCE / EX294 readiness or exam attempt

• Learn OpenShift CLI, projects, pods, deployments, services, routes, RBAC, storage, config maps, secrets, images, builds, troubleshooting
• Use Red Hat OpenShift Local or the Developer Sandbox where appropriate

Evidence

• OpenShift troubleshooting playbook
• EX280 readiness checklist

• Build understanding of AI lifecycle operations, evaluation, monitoring, deployment, CI/CD, governance, reliability

Treat this as a stretch target after the AI apps/agents and Linux automation foundations are stronger.

Evidence

• AI-300 readiness tracker
• Operational AI project or architecture write-up

• Role context: network security engineer working around firewalls, load balancers, infrastructure visibility, automation, and operational security.
• Current skill baseline: moderate Linux, moderate cloud, entry-level Python, limited AI/development background.
• Local environment: RHEL and OpenShift matter internally, so Red Hat certs are the primary Linux path.
• Career direction: AI/automation engineer, technical lead, possibly supervisor later.
• Time model: 5–7 focused hours/week if consistent; 8–10 hours/week compresses the plan.
• Healthcare/security constraint: use sanitized datasets, lab-generated configs, or synthetic tickets only. No PHI, secrets, real configs, or restricted internal data in personal AI tools.

Microsoft AI first: you need vocabulary and context early — AI workloads, generative AI, agents, responsible AI, Azure AI services, Microsoft Foundry.

RHCSA before OpenShift: OpenShift abstracts Linux but doesn't eliminate it. RHCSA gives the OS foundation — users, services, storage, networking, SELinux, containers, troubleshooting.

AI-103 before AI-300: AI-103 is closer to building useful AI apps/agents. AI-300 is more MLOps/GenAIOps-heavy and assumes stronger Python, DevOps, data science, lifecycle knowledge.

RHCE/Ansible before or alongside OpenShift: automation is the bridge between network security engineering and platform/AI operations.

EX280 after containers/Kubernetes basics: EX280 is not where you learn Kubernetes from scratch. Build container/Kubernetes/OpenShift basics first, then validate admin capability.

• Mon/Tue: 60–90 min structured course work.
• Wed: 60–90 min lab work.
• Fri/Sat: 2–3 hours practical build work.
• Sun: 30 min to summarize notes, update the tracker, identify next actions.

Minimum viable weekly output: one page of notes · one lab or command checklist · one small commit, script, diagram, or practical artifact.

Primary path: RHCSA / EX200 → RHCE / EX294 (Ansible focus) → OpenShift Administrator / EX280.

Why not Linux+ first? Linux+ is recognized, but because the environment uses Red Hat and OpenShift, RHCSA is more relevant and career-aligned. RHCSA is also performance-based, which better proves hands-on ability.

Why OpenShift matters: it sits on Linux, containers, Kubernetes, networking, identity/RBAC, storage, and operational troubleshooting. RHCSA makes the OpenShift work less magical; RHCE/Ansible makes it more automatable.

Primary path: AI-901 (fundamentals) → AI-103 (AI apps and agents) → AI-300 later (operationalizing ML and generative AI).

Why not jump straight to DP-100? DP-100 is more data-science and ML-model-lifecycle oriented. Useful later, but the near-term work is more likely AI-enabled operations, automation, agents, workflow improvement, runbooks, and infrastructure-adjacent tooling.

Item 1 — Complete Microsoft AI/ML learning path (Education / Experience): build skills in Microsoft AI, ML concepts, generative AI, agents, Python-based AI apps, and AI operations. Supports growth toward an AI/automation-focused engineering role and applying AI-enabled tooling safely to network security and infrastructure operations.

Item 2 — Pursue Linux administration certification readiness (Education): prepare for a recognized Linux cert on a Red Hat-first path because RHEL and OpenShift are relevant locally. RHCSA first, then RHCE/Ansible automation, with OpenShift administration as the platform follow-up.

Item 3 — Apply AI/automation concepts to network security operations (Experience): build a practical, sanitized proof-of-concept — runbook assistant, ticket summarizer, config diff explainer, or automation reporting tool — to improve repeatability, reduce manual work, and demonstrate safe technical leadership.

• Month 1: completed the AI fundamentals baseline and identified safe, practical AI/automation use cases for infrastructure operations.
• Month 3: building repeatable RHEL administration skills toward RHCSA and documenting commands/labs useful to our environment.
• Month 6: applying AI app/agent concepts to a sanitized network/security workflow, focusing on safe data handling and operational value.
• Month 9: moving from manual Linux administration into Ansible-based repeatable automation.
• Month 12: building OpenShift administration skills that connect Linux, containers, Kubernetes, and platform operations.

• Don't chase every AI cert at once.
• Don't skip RHCSA because you're "moderate" in Linux — the exam is performance-based; comfort is not the same as speed under pressure.
• Don't jump straight into OpenShift admin without container/Kubernetes fundamentals.
• Don't build AI projects with real internal configs, PHI, secrets, or production data unless there's an approved enterprise environment and policy path.
• Don't let Python become endless tutorial watching. Build small tools immediately — even ugly ones.

Certification programs change. Before scheduling any exam, re-check the official credential page.

Microsoft — verify before scheduling:

• Azure AI Fundamentals / AI-901
• Azure AI Apps and Agents Developer Associate / AI-103
• Machine Learning Operations Engineer Associate / AI-300

Red Hat — verify before scheduling:

• Red Hat Certified System Administrator / EX200
• Red Hat Certified Engineer / EX294
• Red Hat Certified OpenShift Administrator / EX280
• Red Hat OpenShift Administration I / DO180 · II / DO280
• Red Hat OpenShift Local · Red Hat Developer Sandbox