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摘要: Rendezvous Architecture helps you run and choose outputs from a Champion model and many Challenger models running in parallel without many overheads. The original approach works well for smaller data sets, so how can this idea adapt to big data pipelines?

摘要: Sooner or later, AI may do something unexpected. If it does, blaming the algorithm won't help.

摘要: 吳恩達近期在 Deeplearning.ai 的一場論壇,說明了自己對於機器學習(Machine Learning)和 MLOps 的最新看法。 在過去,機器學習社群在意的是建模、參數調整、架構選擇,來達到對性能的監控。現在產業運用 AI 的幅度大增,開始轉向了新的階段,一大面臨問題是,當模型投入了生產,許多無法預期的因素,會影響模型性能。

摘要: Machine learning (ML)—the ability for machines to perceive, learn from, abstract, and act on data—has been a catalyst for innovation and advancement across sectors, with national security being no exception.

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