Tag: Google Cloud
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Cloud Blog: Trading in the Cloud: Lessons from Deutsche Börse Group’s cloud-native trading engine
Source URL: https://cloud.google.com/blog/topics/financial-services/lessons-from-deutsche-borse-groups-cloud-native-trading-engine/ Source: Cloud Blog Title: Trading in the Cloud: Lessons from Deutsche Börse Group’s cloud-native trading engine Feedly Summary: Earlier this year, Deutsche Börse Group began developing a new cloud-native, purpose-built trading platform. It was built with a focus on digital assets, such as stablecoins, cryptocurrencies, and other tokenized assets. However, the new…
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Cloud Blog: Empowering retailers with AI for commerce, marketing, supply chains, and more
Source URL: https://cloud.google.com/blog/topics/retail/retail-cpg-ai-partner-ecosystem-nrf-2025/ Source: Cloud Blog Title: Empowering retailers with AI for commerce, marketing, supply chains, and more Feedly Summary: Google Cloud’s mission is to accelerate every organization’s ability to digitally transform its business and industry — and a key part of doing that is with our ISV and service partners, who possess critical industry…
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Cloud Blog: Introducing Vertex AI RAG Engine: Scale your Vertex AI RAG pipeline with confidence
Source URL: https://cloud.google.com/blog/products/ai-machine-learning/introducing-vertex-ai-rag-engine/ Source: Cloud Blog Title: Introducing Vertex AI RAG Engine: Scale your Vertex AI RAG pipeline with confidence Feedly Summary: Closing the gap between impressive model demos and real-world performance is crucial for successfully deploying generative AI for enterprise. Despite the incredible capabilities of generative AI for enterprise, this perceived gap may be…
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Cloud Blog: Distributed data preprocessing with GKE and Ray: Scaling for the enterprise
Source URL: https://cloud.google.com/blog/products/ai-machine-learning/preprocessing-large-datasets-with-ray-and-gke/ Source: Cloud Blog Title: Distributed data preprocessing with GKE and Ray: Scaling for the enterprise Feedly Summary: The exponential growth of machine learning models brings with it ever-increasing datasets. This data deluge creates a significant bottleneck in the Machine Learning Operations (MLOps) lifecycle, as traditional data preprocessing methods struggle to scale. The…