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.

Not necessarily. While some programming experience is helpful, many of my clients have successfully transitioned from web development, data science, or other technical backgrounds. We'll assess your current skills during our strategy call and create a personalized plan that meets you where you are.

Most clients invest 10-15 hours per week, but this can be flexible based on your schedule. We'll have weekly 1:1 calls plus time for you to work on projects and learning. The key is consistency. Regular, focused effort beats occasional marathons.

You become a member of the AI Native Engineer community, and you stay and keep going. Career transitions take different amounts of time for everyone, and I'm not going to abandon you if things take longer. You get ongoing support through good times and bad.

Online courses give you content. 1:1 coaching gives you a personalized roadmap, direct feedback on your work, career strategy, interview prep, and accountability. You get answers to your specific questions and guidance tailored to your unique situation instead of generic advice meant for everyone.

Investment details are discussed during the 30-minute strategy call, where we'll assess your goals and create a custom plan. The program is designed to pay for itself quickly through your increased salary. Most AI engineers see a 20-50% pay increase.

Absolutely. Most of my clients work full-time and make steady progress. We'll schedule calls at times that work for you and create a realistic plan that fits your schedule. Consistency matters more than intensity.