AI Systems Architecture — Ch.07: Engineering and Operations [KR]

The sources discuss key aspects of AI development. AI risk encompasses technical failures, unintended behavior, misuse, and complex environment interactions, especially as systems approach AGI, prompting safety engineering beyond individual model errors. MLOps pipelines integrate data engineering, training, validation, deployment, monitoring, and continuous improvement to operationalize ML models reliably. Hardware‑software co‑design aligns model architecture, processing units, memory hierarchy, and execution frameworks to optimize latency, throughput, energy use, and cost.