Why the transition makes sense
Computer engineering gives a strong base for AI work because production AI systems still need software design, data movement, testing, deployment, and user experience.
Core skills to stack
The transition becomes more manageable when skills are layered carefully: Python, statistics, data cleaning, machine learning fundamentals, model evaluation, APIs, and deployment.
type LearningArea = "software" | "data" | "models" | "deployment";
const focus: LearningArea[] = ["software", "data", "models", "deployment"];Building a portfolio
Projects should show the full path from problem framing to usable output. A smaller complete project is often stronger than a large unfinished experiment.