🚀 24-Week Intensive • Self-Paced • Free Forever

From Zero to AI Infrastructure Engineer

Master DevOps, MLOps, and LLMOps with a battle-tested curriculum. Build real projects, earn certifications, and land $150K+ senior roles in AI/ML infrastructure.

350+ Checkpoints
24 Weeks
12+ Portfolio Projects
$150K+ Target Salary

What You'll Master

🐧

Linux & Shell

Navigate any system, write production scripts, manage services

🐳

Docker & K8s

Containerize anything, orchestrate at scale with Kubernetes

☁️

Cloud & IaC

AWS, Azure, GCP + Terraform for infrastructure as code

🔄

CI/CD & GitOps

GitHub Actions, ArgoCD, automated deployments

📊

Observability

Prometheus, Grafana, logging, tracing, alerting

🤖

AI/ML Infrastructure

MLOps, LLMOps, vLLM, RAG systems, GPU orchestration

The Learning System

Every concept follows the same mastery framework

1

Learn

Study the concept through curated resources

2

Lab

Hands-on practice in real environments

3

Build

Create a project that proves competence

4

Document

Write runbooks like a senior engineer

Mastery = Can explain it + Apply it + Debug it + Write a runbook for it

Career Outcomes

Train for senior-level roles in AI infrastructure

🎯 Target Role

Senior MLOps Engineer

$150K – $220K
🔥 High Demand
🎯 Target Role

AI Infrastructure Engineer

$144K – $270K
📈 Growing Fast
Entry Point

DevOps Engineer

$80K – $120K
Many Openings
Entry Point

Cloud Engineer

$90K – $140K
Growing

24-Week Roadmap

Weeks 1-6

Foundations

Linux, Shell, Git, Python, Networking

Weeks 7-10

Containers

Docker, Compose, Image optimization

Weeks 11-14

Cloud & IaC

AWS, Azure, Terraform modules

Weeks 15-18

Kubernetes

K8s, Helm, GitOps, ArgoCD

Weeks 19-21

Observability

Prometheus, Grafana, SRE practices

Weeks 22-24

AI/ML Infrastructure

MLOps, LLMOps, vLLM, RAG systems

Everything You Need

📊

Progress Tracking

Track every checkpoint across 350+ items. Syncs across devices when signed in.

📧

Email Reports

Weekly progress summaries and motivation nudges delivered to your inbox.

📱

Apple Notes Export

Export your progress to Apple Notes for offline tracking on any device.

🎯

Curated Resources

Hand-picked YouTube, Udemy, Coursera, and documentation links for every topic.

📋

Portfolio Projects

12+ production-quality projects with runbooks, diagrams, and documentation.

🏆

Cert Prep

Prepare for AWS, Azure, CKA, Terraform, and other industry certifications.

Prepare for Industry Certifications

Ready to Transform Your Career?

No signup walls. No paywalls. No email capture. Just a battle-tested curriculum to take you from zero to AI infrastructure engineer.

Free forever. Your progress syncs across devices. Cancel anytime (there's nothing to cancel).

Senior-Level AI Infrastructure Training

This curriculum trains you to senior-level competence in AI/ML Infrastructure and LLMOps. Not surface-level tutorials — deep, production-grade knowledge. You'll drill every concept until you can explain it, build it, debug it, and teach it.

Senior Depth of training, not just intro
24 wks Intensive, structured curriculum
AI/ML MLOps, LLMOps, GPU, vLLM, RAG
$150K+ Target senior-level salaries

What Is This Program?

A specialized training accelerator for AI/ML Infrastructure, MLOps, and LLMOps. You start with DevOps fundamentals, then go deep into the systems that power production AI — model serving, GPU orchestration, RAG pipelines, and LLM inference optimization. The goal: senior-level competence in a niche where demand outstrips supply.

  • Foundation first: Linux, containers, Kubernetes, cloud, IaC — the base layer
  • Then specialization: MLflow, vLLM, Ray, vector DBs, GPU clusters, LLM serving
  • Senior-level depth: Not surface tutorials — production patterns, failure modes, scaling
  • Portfolio of AI infra projects: Real systems that prove you can deploy ML at scale
  • Cert-prep included: AWS, Azure, Kubernetes, Terraform — credentialing matters

Generic DevOps is crowded. AI Infrastructure is not. This curriculum positions you for the less-competed, higher-paid roles.

Career Path: Senior-Level Training, Accelerated Timeline

This curriculum trains you to senior-level competence — not just entry-level basics. You'll drill every concept deep: production patterns, failure modes, scaling strategies, debugging skills. Your first job title might be mid-level, but you'll have senior-level knowledge from day one.

💡 Why This Matters

Most bootcamps teach you to pass interviews. This curriculum teaches you to actually do the job at a senior level. The depth means you'll progress faster, get promoted quicker, and command higher salaries because you're not learning on the job — you already know it.

🎯 What You're Trained For — Senior-Level Roles

These are the roles you'll be qualified for after completing this curriculum. Your first title may vary, but your skills won't.

Senior MLOps Engineer $150K – $220K 🔥 High Demand
Senior AI Infrastructure Engineer $144K – $270K 🔥 Growing Fast
Senior ML Platform Engineer $160K – $240K 📈 Strong Demand
Senior SWE, Infrastructure AI $180K – $280K 📈 Big Tech Hiring

📍 Realistic First Titles — Your Skills Exceed the Title

Companies may hire you at these titles first (especially without prior experience), but you'll perform at senior level and get promoted fast.

MLOps Engineer $110K – $160K 🔥 High Demand
ML Platform Engineer $120K – $170K 📈 Growing
ML Infrastructure Engineer $115K – $165K ⚡ Specialized
DevOps/Cloud Engineer $80K – $120K 🔥 Many Openings

🚀 Staff/Principal Track — Long-Term Trajectory

With this foundation + 3-5 years experience, these become realistic targets.

Staff ML Engineer $220K – $350K+ ⭐ Selective Hiring
Principal ML Systems Architect $280K – $450K+ 🏆 Top 5%
AI Infrastructure Lead/Manager $200K – $320K 📈 Growing Need
Director of ML Platform $250K – $400K+ 🏆 Leadership
💡 Senior Knowledge ≠ Senior Title (At First)

You'll leave this curriculum with senior-level depth — but without prior work experience, your first title might be mid-level. That's OK. You'll outperform peers, get promoted fast, and reach senior titles in 1-2 years instead of 4-5. The knowledge compounds.

* Salary data from Indeed, Levels.fyi, Glassdoor (2024-2025). US-based; varies by city (SF/NYC +20-30%, remote/international -20-40%). These are mid-level ranges — entry-level is typically lower. Always verify for your specific market.

The Hard Truth About AI/ML Infrastructure Jobs

AI Infrastructure is a real career path, but let's be honest about what you're up against — especially for MLOps and LLMOps:

🧗 Junior MLOps Roles Are Rare Most MLOps postings want 2-4+ years experience. "Junior MLOps Engineer" barely exists. You'll likely start in DevOps/Cloud, then specialize.
🔀 You Need Both Skills MLOps requires infra skills (K8s, cloud, IaC) AND ML understanding. Most people have one or the other — that's the barrier, and the opportunity.
📊 LLMOps Is Brand New LLMOps roles exploded in 2023-2024. Salary data is limited, role definitions vary, and expectations are still forming. Opportunity + uncertainty.
🚫 Ghost Jobs Still Apply AI hype means companies post ML roles they may never fill. Verify by checking team size on LinkedIn and asking about hiring timeline.
🏢 Dedicated Roles = Bigger Companies Startups want "full-stack ML" (you do everything). Dedicated MLOps/Platform roles are more common at mid-size+ companies with real ML scale.
⏰ It's a Multi-Year Journey Realistic: 6mo training → 3-12mo job search → 1-2yrs in foundation role → specialize. The shortcut everyone wants doesn't exist.
Why It's Still Worth It

Yes, it's hard. But AI infrastructure skills are genuinely scarce. Once you have real experience deploying models, managing GPU clusters, or building ML pipelines — you become hard to replace. The pay is real, the demand is real, and the skills transfer across industries. This curriculum gives you a structured path — but no path is short.

⏱️ The Lifestyle Commitment — What It Actually Takes

This isn't a "watch videos on the weekend" program. Here's what your life needs to look like to make this work:

📅 During Training (24 Weeks / ~6 Months)

2-4 hrs Daily Active learning, labs, hands-on practice. Not passive video watching.
15-25 hrs Weekly Total time including review, project work, and documentation.
6+ months Consistent No 2-week breaks. Consistency beats intensity. Show up every day.

🌅 A Realistic Day (If You Have a Full-Time Job)

5:30 AM – 7:30 AM Morning block: Study + lab before work (best focus time)
12:00 PM – 12:45 PM Lunch: Review notes, read docs, watch short videos
7:00 PM – 9:00 PM Evening block: Project work, practice, build portfolio pieces
Weekend 4-6 hours: Deep project work, catch up, week review

🚫 What You'll Need to Cut Back On

  • Scrolling/social media: Ruthlessly reduce. Doom-scrolling destroys focus.
  • Netflix/gaming: Treat as rewards, not defaults. 1 episode, not 1 season.
  • Social events: Say no more often. Friends will understand (real ones do).
  • Sleep debt: Don't sacrifice sleep — it destroys retention. Protect 7+ hours.
  • "I'll start Monday": Kill this mindset. Start today. Start ugly. Start anyway.

🧠 The Mental Game (This Is the Hard Part)

Week 1-4: Excited, motivated, this is doable!
Week 5-10: The grind hits. Kubernetes is confusing. You want to quit.
Week 11-18: Plateau. Progress feels invisible. Imposter syndrome peaks.
Week 19-24: Things click. You build real stuff. Confidence grows.
Job search: Rejection is constant. 50+ apps before interviews. It's a numbers game.

📆 The Full Timeline (Be Realistic)

Months 1-6 Training Phase 24 weeks of curriculum. Build foundation + portfolio.
Months 6-12 Job Search Phase Active applications, networking, interviews. Keep learning while searching.
Months 12-24 Foundation Role First job (likely DevOps/Cloud). Learn production realities. Build credibility.
Year 2-3+ Specialization Move into MLOps/AI Infra with real experience. Senior track begins.
🚨 If This Sounds Like Too Much

Then it probably is — and that's okay. Not everyone wants to make these trade-offs, and there's no shame in that. But if you want the career outcomes on this page, this is what it costs. There's no hack, no shortcut, no "learn in 4 weeks" magic. The people who succeed treat this like a second job for 6+ months.

🔥 If This Sounds Exactly Right

Then let's go. You're not looking for easy — you're looking for worth it. The grind is temporary. The skills compound. The career lasts decades. Start Week 1. Show up every day. Document everything. Build in public. You've got this.

Certifications This Curriculum Prepares You For

AWS Cloud Practitioner Foundational
AWS Solutions Architect Associate Associate
AWS DevOps Engineer Professional Professional
Azure AZ-900 Fundamentals Foundational
Azure AZ-104 Administrator Associate
GCP Cloud Digital Leader Foundational
CNCF CKA (Kubernetes Admin) Professional
CNCF CKAD (K8s Developer) Professional
HashiCorp Terraform Associate Associate
HashiCorp Vault Associate Associate
Linux Foundation LFCS (Linux SysAdmin) Professional
Docker DCA (Docker Certified) Professional

Why AI Infrastructure? The Strategic Advantage

  • AI is the new mobile: Every company is deploying ML/LLMs — they need people who can actually ship it
  • Supply-demand mismatch: ML engineers build models; few know how to deploy them at scale
  • Higher pay, less competition: AI infra roles pay 20-40% more than generic DevOps, with fewer applicants
  • Future-proof: AI infrastructure is growing, not shrinking — unlike some traditional ops roles
  • Senior-level faster: Specialization lets you skip the crowded mid-level DevOps pool
  • Remote-friendly: Most AI/ML infra work can be done anywhere with a terminal

Who This Is For

  • DevOps/SRE engineers: Want to specialize in the highest-growth area
  • Backend developers: Interested in the ML deployment side, not just model training
  • Data engineers: Looking to move into ML infrastructure and model serving
  • Career changers: Targeting a niche with less competition than generic DevOps
  • ML engineers: Want to understand the infra side of production ML systems
  • Anyone serious: Willing to put in 24 weeks of focused work for a real outcome

Tech Stack: Foundation → AI/ML Specialization

Weeks 1-18: Build the infrastructure foundation. Weeks 19-24: Specialize in AI/ML systems.

🤖 AI/ML Infrastructure (The Specialization)
vLLM TGI Ray MLflow Kubeflow Vector DBs GPU Orchestration LangChain
Core Infrastructure
Linux Bash Python Git Networking
Containers & Orchestration
Docker Kubernetes Helm NVIDIA GPU Operator
Cloud Platforms
AWS Azure GCP Terraform
CI/CD & MLOps Pipelines
GitHub Actions ArgoCD ML Pipelines Model Registry
Observability & ML Monitoring
Prometheus Grafana Model Drift Detection GPU Metrics

The Learning System: How This Actually Works

1 Learn 45-90 min daily studying concepts from the curriculum
→
2 Lab 45-90 min hands-on practice in real environments
→
3 Build Weekly mini-project applying what you learned
→
4 Ship Deploy to GitHub with docs, diagrams, and runbooks
📁 GitHub Portfolio 12+ production-quality projects with documentation
📋 Runbooks Operational docs showing you can think like a senior engineer
📊 Architecture Diagrams Visual documentation of every system you build
📓 Ops Journal Daily log of problems solved — interview gold

24-Week Roadmap Preview

Phase 1 Foundations Weeks 1-6
Linux mastery, shell scripting, Git workflows, Python automation, networking fundamentals. Outcome: Can navigate any Linux system and automate basic tasks.
Phase 2 Containerization Weeks 7-10
Docker deep dive, multi-stage builds, Compose, container security, image optimization. Outcome: Can containerize any application and deploy it reliably.
Phase 3 Cloud & IaC Weeks 11-14
AWS/Azure core services, Terraform for infrastructure as code, state management, modules. Outcome: Can provision and manage cloud infrastructure programmatically.
Phase 4 Kubernetes Weeks 15-18
K8s architecture, deployments, services, ingress, Helm charts, GitOps with ArgoCD. Outcome: Can deploy and manage production Kubernetes clusters.
Phase 5 Observability & SRE Weeks 19-21
Prometheus, Grafana, alerting, SLOs/SLIs, incident response, chaos engineering basics. Outcome: Can build and operate observable, reliable systems.
Phase 6 AI/ML Infrastructure Weeks 22-24
MLOps fundamentals, model serving (vLLM, TGI), GPU infrastructure, LLMOps patterns. Outcome: Can deploy and scale ML/AI systems in production.

What Makes This Different

Bootcamps $15K+ and 3 months of surface-level exposure
YouTube Scattered, no structure, easy to get lost
Udemy Passive watching, outdated content, no portfolio
This Curriculum Free, structured, hands-on, portfolio-focused, cert-prep included

Success Requires Commitment

  • 2-3 hours daily: This is not a "watch while cooking" program
  • Consistent practice: Skills decay fast — daily reps matter
  • Build in public: Push to GitHub, write about what you learn
  • Embrace failure: You'll break things. That's the point.
  • Community: Join Discord/Slack groups to stay accountable

Your Learning Contract

To maximize your success, commit to these practices:

📓
Daily Ops Journal Log what broke, why, how you fixed it, and how to prevent it. This becomes interview gold.
📊
Architecture Diagrams Draw every system you build. Ugly diagrams are fine. The habit matters.
📋
Runbooks for Every Project Document: deploy steps, verification, rollback, common failures. Think like an SRE.
🎥
Weekly Teach-Back Record 2 minutes explaining one concept. If you can't explain it, you don't know it.

Ready to Start?

Everything you need is in this single page. No signup, no paywall, no email capture. Just you, the curriculum, and your commitment to ship.

Set Up Your Environment → Explore the Curriculum View the 24-Week Plan

Definition of "I learned it": You can explain it, apply it, debug it, and write a runbook for it. That's the standard.