Cloud Computing

Cloud CISO Perspectives: The high security cost of legacy tech

Welcome to the first Cloud CISO Perspectives for November 2024. Today I’m joined by Andy Wen, Google Cloud’s senior director of product management for Google Workspace, to discuss a new Google survey into the high security costs of legacy tech. As with all Cloud CISO Perspectives, the contents of this newsletter are posted to the […]

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How to deploy Llama 3.2-1B-Instruct model with Google Cloud Run GPU

As open-source large language models (LLMs) become increasingly popular, developers are looking for better ways to access new models and deploy them on Cloud Run GPU. That’s why Cloud Run now offers fully managed NVIDIA GPUs, which removes the complexity of driver installations and library configurations. This means you’ll benefit from the same on-demand availability

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Pirates in the Data Sea: AI Enhancing Your Adversarial Emulation

Matthijs Gielen, Jay Christiansen Background New solutions, old problems. Artificial intelligence (AI) and large language models (LLMs) are here to signal a new day in the cybersecurity world, but what does that mean for us—the attackers and defenders—and our battle to improve security through all the noise? Data is everywhere. For most organizations, the access

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Empower your teams with self-service Kubernetes using GKE fleets and Argo CD

Managing applications across multiple Kubernetes clusters is complex, especially when those clusters span different environments or even cloud providers. One powerful and secure solution combines Google Kubernetes Engine (GKE) fleets and, Argo CD, a declarative, GitOps continuous delivery tool for Kubernetes. The solution is further enhanced with Connect Gateway and Workload Identity. This blog post

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Data loading best practices for AI/ML inference on GKE

As AI models increase in sophistication, there’s increasingly large model data needed to serve them. Loading the models and weights along with necessary frameworks to serve them for inference can add seconds or even minutes of scaling delay, impacting both costs and the end-user’s experience.  For example, inference servers such as Triton, Text Generation Inference

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65,000 nodes and counting: Google Kubernetes Engine is ready for trillion-parameter AI models

As generative AI evolves, we’re beginning to see the transformative potential it is having across industries and our lives. And as large language models (LLMs) increase in size — current models are reaching hundreds of billions of parameters, and the most advanced ones are approaching 2 trillion — the need for computational power will only

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Unlocking LLM training efficiency with Trillium — a performance analysis

Rapidly evolving generative AI models place unprecedented demands on the performance and efficiency of hardware accelerators. Last month, we launched our sixth-generation Tensor Processing Unit (TPU), Trillium, to address the demands of next-generation models. Trillium is purpose-built for performance at scale, from the chip to the system to our Google data center deployments, to power

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Emerging Threats: Cybersecurity Forecast 2025

Every November, we start sharing forward-looking insights on threats and other cybersecurity topics to help organizations and defenders prepare for the year ahead. The Cybersecurity Forecast 2025 report, available today, plays a big role in helping us accomplish this mission. This year’s report draws on insights directly from Google Cloud’s security leaders, as well as

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How Deutsche Bank built a new retail data platform on Google Cloud

Getting insights into customer’s preferences and needs is crucial for any modern business — and that’s especially true for a retail bank. Insights from customer data help deliver improved customer experiences through custom tailored products, better services, and higher levels of automation. But to gain these customer insights, you need your input data to be

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