Learn MLOps for AI Career
The Practical Path.

MLOps is the practice of deploying and maintaining ML models in production reliably and efficiently. It combines ML, DevOps, and data engineering. It matters because 87% of ML models never make it to production - MLOps engineers solve this gap. They build the infrastructure that turns experimental models into business value.

Your DevOps foundation is a massive advantage. You already understand CI/CD, containers, and infrastructure-as-code. Focus on learning: 1) ML fundamentals (not deep theory, just how models work), 2) Data pipeline patterns (feature engineering, data versioning), 3) Model-specific challenges (drift detection, A/B testing for ML). Most DevOps engineers can be job-ready for MLOps roles in 3-4 months of focused learning.

Core skills: 1) Container orchestration (Kubernetes) for model serving, 2) ML pipeline tools (one of: Kubeflow, Airflow, or Prefect), 3) Model registries and versioning (MLflow, DVC), 4) Monitoring and observability for ML systems, 5) Feature stores (Feast, Tecton). You don't need all tools - pick one stack and go deep. Companies care about patterns, not specific vendor experience.

For developers with existing DevOps or ML background: 3-4 months of dedicated learning (10-15 hours/week) to be job-ready. For complete beginners to both DevOps and ML: 6-9 months. The key is building real projects - reading documentation won't get you hired. One deployed end-to-end pipeline is worth more than 10 certificates.

Certifications alone won't land you an MLOps role. They can help pass HR filters, but hiring managers want to see real projects. If you pursue certifications, AWS Machine Learning Specialty or Google Professional ML Engineer are most recognized. But prioritize building a portfolio of deployed ML systems first - that's what gets you past technical interviews.

MLOps is one of the fastest-growing specializations in tech. Average salaries range from $150K-$200K in major markets. Demand is driven by every company trying to operationalize AI. The role goes by many titles: MLOps Engineer, ML Platform Engineer, ML Infrastructure Engineer. Your competition is lower than pure ML roles because fewer people have the hybrid DevOps + ML skillset.

It probably isn't, and you should hold the money. Most cohort dropouts are people who couldn't articulate what they were shipping when they signed up. That's why the consult exists, and why I turn down most applications. If we get on the call and you can't tell me what you'll have shipped at the end of week 8, I'll point you to the AI Native Engineer community until you can.

Yes, the cohort works for people shipping their first serious AI system whether the goal is to land a senior role or to launch a product. The shipped system serves both equally well.

You need to be able to code in Python or TypeScript. Complete beginners can follow the classroom they get access to before the cohort sessions to come in well-prepared.

You'll spend 3 hours every Tuesday in the live session and roughly 3 hours of async work in between, for 8 weeks. The Tuesday session time is fixed.

It's a four-figure investment that we discuss during the 30-minute consult, alongside whether the cohort is the right fit for your project.

Yes, most attendees do. The live session is one Tuesday a week and the async work fits around your existing schedule, as long as you can carve out roughly 6 hours a week.