Tag: Kubernetes Engine
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Cloud Blog: GKE delivers breakthrough Horizontal Pod Autoscaler performance
Source URL: https://cloud.google.com/blog/products/containers-kubernetes/rearchitected-gke-hpa-improves-scaling-performance/ Source: Cloud Blog Title: GKE delivers breakthrough Horizontal Pod Autoscaler performance Feedly Summary: At Google Cloud, we are committed to providing the fastest and most reliable Kubernetes platform, Google Kubernetes Engine (GKE). Today, we are excited to announce an improved Horizontal Pod Autoscaler (HPA), the Kubernetes feature that automatically updates workload resources…
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Cloud Blog: C4A, the first Google Axion Processor, now GA with Titanium SSD
Source URL: https://cloud.google.com/blog/products/compute/first-google-axion-processor-c4a-now-ga-with-titanium-ssd/ Source: Cloud Blog Title: C4A, the first Google Axion Processor, now GA with Titanium SSD Feedly Summary: Today, we are thrilled to announce the general availability of C4A virtual machines with Titanium SSDs custom designed by Google for cloud workloads that require real-time data processing, with low-latency and high-throughput storage performance. Titanium…
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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: 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…