This roadmap is for everyone — complete beginners, working DevOps engineers, and ML practitioners. You do not need to start at Step 1. Pick the entry point that matches where you are right now and follow your path forward.
* Begin at OS & Linux Fundamentals — no prior experience needed * Work through every section in order, top to bottom * Each section builds directly on the one before it * Budget roughly 6 to 12 months of consistent study and practice * Focus on understanding concepts before memorizing commands * You will be job-ready at the AIOps Engineer level by the Capstone
* Skip Core Engineering Foundations if you are comfortable with Linux, Python, Docker, and Kubernetes * Start at Monitoring & Observability and work forward from there * Skim Distributed Systems & Reliability if you have not worked with CAP theorem or circuit breakers * Focus your energy on ML, Prompt Engineering, and Agent sections — that is the new layer on top of your existing skills * Budget roughly 3 to 5 months to reach Capstone from this entry point
* You understand models, training, and evaluation — but ops context is likely missing * Start at Monitoring & Observability to understand the data sources AIOps models consume * Pay close attention to Distributed Systems — failure modes in production are very different from clean datasets * The Prompt Engineering and Agent Orchestration sections will be your highest-leverage new skills * Budget roughly 3 to 4 months to reach Capstone from this entry point
* You likely know everything through Distributed Systems already * Start at Data Pipelines & Streaming and work forward * The ML, Prompt Engineering, Model Ecosystem, and Agent sections are your core new territory * The Capstone projects are designed to produce portfolio work you can present in senior interviews * Budget roughly 2 to 3 months of focused evenings and weekends
Begin at OS & Linux Fundamentals — no prior experience needed Work through every section in order, top to bottom Each se...
Skip Core Engineering Foundations if you are comfortable with Linux, Python, Docker, and Kubernetes Start at Monitoring ...
You understand models, training, and evaluation — but ops context is likely missing Start at Monitoring & Observability ...
You likely know everything through Distributed Systems already Start at Data Pipelines & Streaming and work forward The ...
Aligns directly with DevOps, Site Reliability (SRE), and Platform Engineering job descriptions.