Cloud Computing

Securing the mission: Google Public Sector’s CMMC Level 2 certification and commitment to national security

Cloud Computing /

Google Public Sector is committed to supporting the critical missions of the U.S. Department of Defense (DoD) by delivering cutting-edge cloud, AI, and data services securely. Today, we are marking an important milestone in that commitment: we have successfully achieved Cybersecurity Maturity Model Certification (CMMC) Level 2 certification under the DoD’s CMMC program. This certification, […]

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How Ericsson achieves data integrity and superior governance with Dataplex

Cloud Computing /

Data is the engine of modern telecommunications. For Ericsson’s Managed Services, which operates a global network of more than 710,000 sites, harnessing this data is not just an advantage, it’s essential for business growth and leadership. To power the future of its autonomous network operations and deliver on its strategic priorities, Ericsson has been on

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AlloyDB accelerates AI with automated vector indexing and embedding

Cloud Computing /

Modern applications store their most valuable data such as product catalogs or user profiles in operational databases. These data stores are excellent for applications that need to handle real-time transactions — and with their support for vector operations, they’ve also become an excellent foundation for modern search or gen AI application serving. AlloyDB AI provides

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Google Cloud Europe establishes new European Advisory Board

Cloud Computing /

Across the world, organizations are partnering with Google Cloud to tackle their toughest challenges, drive digital transformation, and unlock new levels of growth. In Europe, organizations face unique and complex regulatory challenges. To ensure we’re delivering the best possible value and experience for our customers here, we have established a new European Advisory Board. This

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Boosting LLM Performance with Tiered KV Cache on Google Kubernetes Engine

Cloud Computing /

Large Language Models (LLMs) are powerful, but their performance can be bottlenecked by the immense NVIDIA GPU memory footprint of the Key-Value (KV) Cache. This cache, crucial for speeding up LLM inference by storing Key (K) and Value (V) matrices, directly impacts context length, concurrency, and overall system throughput. Our primary goal is to maximize

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