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School Security & Active Shooter Interdiction: A Q&A With Vince Riden

Security Industry Association

It’s important to promote a safe learning environment for every student and protect the teachers, staff and visitors in our schools, and SIA appreciates the many talented security professionals who are working diligently each day to enhance the safety and security of our schools and mitigate active shooter threats. More is better.

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Digital Twins and the Future of Physical Security and Risk Management

Security Industry Association

Today’s technology advances, such as cloud computing, deep learning and IoT, enable the application of enterprise data to mitigate risks and accurately and efficiently manage facilities’ security systems. It also mitigates operational costs associated with outside contractors, errors, rework and compliance breaches.

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Storage and Data Protection News for the Week of September 27; Updates from Hitachi Vantara, Pure Storage, Rubrik & More

Solutions Review

Benchmark on Storage Systems & AI Model Training The MLPerf Storage benchmark is the first and only open, transparent benchmark to measure storage performance in a diverse set of ML training scenarios. Read on for more MLPerf Releases Storage v1.0 NetApp’s E-Series is already SuperPOD-certified.

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Pure Storage Raises the Bar with NVIDIA Cloud Partners and AI Data Platform

Pure Storage

Pure Storage Raises the Bar with NVIDIA Cloud Partners and AI Data Platform by Pure Storage Blog Summary Pure Storage has achieved high-performance storage (HPS) certification for NVIDIA Cloud Partner (NCP) reference architectures.

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How to Implement Threat Modeling in Your DevSecOps Process

Pure Storage

Threat modeling is an essential tool for developers and security professionals to identify and mitigate potential security risks in software systems proactively. Attack Method STRIDE Category Design Hardening Steps Mitigating Controls Credential Stuffing Spoofing Implement multi-factor authentication.

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TPUs vs. GPUs: What’s the Difference?

Pure Storage

GPUs are generally faster than CPUs for deep learning tasks, but the specialized architecture of TPUs often allows them to be faster than GPUs. Benchmarks comparing TPUs and GPUs in machine learning tasks have shown that TPUs often outperform GPUs in terms of training speed and efficiency.

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TPUs vs. GPUs: What’s the Difference?

Pure Storage

GPUs are generally faster than CPUs for deep learning tasks, but the specialized architecture of TPUs often allows them to be faster than GPUs. Benchmarks comparing TPUs and GPUs in machine learning tasks have shown that TPUs often outperform GPUs in terms of training speed and efficiency.