Tag: GKE

  • Cloud Blog: How retailers are accelerating AI into production with NVIDIA and Google Cloud

    Source URL: https://cloud.google.com/blog/topics/retail/how-retailers-are-accelerating-ai-with-nvidia-and-google-cloud/ Source: Cloud Blog Title: How retailers are accelerating AI into production with NVIDIA and Google Cloud Feedly Summary: Retailers have always moved quickly to connect and match the latest merchandise with customers’ needs. And the same way they carefully design every inch of their stores, the time and thought that goes into…

  • 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…

  • Cloud Blog: Scaling to zero on Google Kubernetes Engine with KEDA

    Source URL: https://cloud.google.com/blog/products/containers-kubernetes/scale-to-zero-on-gke-with-keda/ Source: Cloud Blog Title: Scaling to zero on Google Kubernetes Engine with KEDA Feedly Summary: For developers and businesses that run applications on Google Kubernetes Engine (GKE), scaling deployments down to zero when they are idle can offer significant financial savings. GKE’s Cluster Autoscaler efficiently manages node pool sizes, but for applications…

  • Cloud Blog: Scaling to zero on Google Kubernetes Engine with KEDA

    Source URL: https://cloud.google.com/blog/products/containers-kubernetes/scale-to-zero-on-gke-with-keda/ Source: Cloud Blog Title: Scaling to zero on Google Kubernetes Engine with KEDA Feedly Summary: For developers and businesses that run applications on Google Kubernetes Engine (GKE), scaling deployments down to zero when they are idle can offer significant financial savings. GKE’s Cluster Autoscaler efficiently manages node pool sizes, but for applications…

  • Cloud Blog: The Year in Google Cloud – 2024

    Source URL: https://cloud.google.com/blog/products/gcp/top-google-cloud-blogs/ Source: Cloud Blog Title: The Year in Google Cloud – 2024 Feedly Summary: If you’re a regular reader of this blog, you know that 2024 was a busy year for Google Cloud. From AI to Zero Trust, and everything in between, here’s a chronological recap of our top blogs of 2024, according…

  • Cloud Blog: Using Cilium and GKE Dataplane V2? Be sure to check out Hubble for observability

    Source URL: https://cloud.google.com/blog/products/containers-kubernetes/using-hubble-for-gke-dataplane-v2-observability/ Source: Cloud Blog Title: Using Cilium and GKE Dataplane V2? Be sure to check out Hubble for observability Feedly Summary: As a Kubernetes platform engineer, you’ve probably followed the buzz around eBPF and its revolutionary impact on Kubernetes networking. Perhaps you’ve explored Cilium, a popular solution leveraging eBPF, and wondered how Google…

  • Cloud Blog: Scaling to zero on Google Kubernetes Engine with KEDA

    Source URL: https://cloud.google.com/blog/products/containers-kubernetes/scale-to-zero-on-gke-with-keda/ Source: Cloud Blog Title: Scaling to zero on Google Kubernetes Engine with KEDA Feedly Summary: For developers and businesses that run applications on Google Kubernetes Engine (GKE), scaling deployments down to zero when they are idle can offer significant financial savings. GKE’s Cluster Autoscaler efficiently manages node pool sizes, but for applications…

  • Cloud Blog: Announcing the general availability of Trillium, our sixth-generation TPU

    Source URL: https://cloud.google.com/blog/products/compute/trillium-tpu-is-ga/ Source: Cloud Blog Title: Announcing the general availability of Trillium, our sixth-generation TPU Feedly Summary: The rise of large-scale AI models capable of processing diverse modalities like text and images presents a unique infrastructural challenge. These models require immense computational power and specialized hardware to efficiently handle training, fine-tuning, and inference. Over…