arXiv:2606.16842v1 Announce Type: cross Abstract: Teaching Software Engineering for AI-enabled systems entails addressing the integration of AI components within full-scale software architectures under realistic constraints. While machine learning courses emphasize model development, students often lack experience in architectural design, deployment, and monitoring of AI-enabled systems. Empirical evaluations of such system-oriented AI courses remain limited. This paper reflects on the design and implementation of a project-based master's-level course titled AI Algorithms: Theory and Engineeri
The rapid deployment and increasing complexity of AI systems necessitate a renewed focus on robust software engineering principles for their integration and lifecycle management.
Improving the engineering of AI-enabled systems is crucial for their reliability, scalability, and ethical deployment, impacting all sectors adopting AI.
Educational curricula are adapting to emphasize full-stack integration and operational aspects of AI, moving beyond purely model-centric development.
- · Software engineers with AI integration skills
- · Companies building robust AI products
- · Educational institutions offering project-based AI engineering courses
- · AI developers focused solely on model development
- · Organizations with siloed AI and software engineering teams
Increased availability of AI systems that are reliable and maintainable in production environments.
Faster adoption of complex AI functionalities across industries due to improved engineering practices.
Elevated standards for AI system safety and compliance, driven by better integration and monitoring capabilities.
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Read at arXiv cs.AI