Western Governors University 3 credits AI Engineering
This course examines computer systems and architectures through the lens of AI engineering, emphasizing how hardware and software decisions shape the feasibility, performance, cost, and reliability of modern AI workloads. Learners analyze the roles of CPUs, GPUs, TPUs, memory hierarchies, storage, interconnects, and distributed systems in enabling efficient training and inference. The course emphasizes architectural tradeoffs, profiling and bottleneck diagnosis, and resource management strategies that influence latency, throughput, scalability, and operational constraints. Through applied analysis and system design activities, learners translate functional and non-functional requirements into concrete compute, memory, bandwidth, and power constraints across cloud, embedded, and edge environments. Learners evaluate failure modes and safe degradation behaviors and apply infrastructure foundations, including standardized runtime environments and repeatable configuration concepts, to support defensible deployment decisions. By the end of the course, learners can design and justify system architectures aligned to real-world AI production constraints and organizational objectives. pipelines, apply word embeddings, and use sequence-based and transformer-based models to complete tasks such as classification, sentiment analysis, and question answering. The course emphasizes rigorous evaluation beyond accuracy, using task-appropriate metrics, structured error analysis, and stress testing under realistic input variation, to identify failure modes and reliability risks. Learners also assess bias, safety, and responsible-use concerns (including generative AI risks when applicable) and produce governance artifacts that document intended use, limitations, evaluation results, and monitoring expectations. Through applied engineering work, learners demonstrate judgment in selecting methods and designing solutions that meet technical, ethical, and operational constraints.
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