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AI Workflow - Page 15

An AI workflow is the end-to-end sequence used to turn a problem and its data into a functioning artificial intelligence system. Depending on the application, it may include data collection, cleaning, labeling, model selection, training, evaluation, deployment, monitoring, and feedback. Generative AI workflows can also add retrieval, prompt construction, tool calls, guardrails, and human approval steps. Mapping the workflow makes dependencies and failure points visible: poor inputs can undermine a strong model, while missing monitoring can allow quality to decline unnoticed. Well-designed workflows define ownership, measurable acceptance criteria, data-handling rules, and fallback behavior for cases the system cannot handle safely.

Nvidia Invests $6.5 Billion in Technology That Could Reshape AI Infrastructure
By • 3 mins read
AI & Machine Learning, Cloud & Infrastructure, News, Research & Innovation

Nvidia Invests $6.5 Billion in Technology That Could Reshape AI Infrastructure

By • 3 mins read

Nvidia has committed at least $6.5 billion to photonics companies in recent months as it seeks to overcome AI infrastructure bottlenecks. The investments target optical technologies that could reduce energy consumption and improve data transfer across future AI systems.