Role titles vary across organizations. For this learning path, AI engineering emphasizes building and operating model-powered applications. ML engineering often emphasizes training pipelines, model serving, and the lifecycle of learned models. Data science often emphasizes analysis, experiments, and deriving insights or predictions from data. Real jobs can include parts of all three.
Use responsibilities to plan learning rather than relying only on a title. A role that owns a recommendation model requires deeper training and statistical evaluation work. A role that builds an internal knowledge assistant requires strong retrieval, permissions, application integration, and operations.
The common foundation is programming, data handling, experimental reasoning, and clear communication. This curriculum teaches that foundation before specialization. It does not promise that every employer uses the same role boundaries.
Exercise: write a responsibility map for your target project: data preparation, model choice, application API, evaluation, deployment, and user feedback. Identify which skills you have yet to learn.
Check: you can connect each learning stage to a responsibility instead of collecting tool names.