I'm not an ML engineer. I built one anyway.
The article discusses the challenges of implementing machine learning on ARM edge devices. It highlights the limitations of traditional cloud-native observability tools in constrained environments. The author shares insights on using eBPF for efficient anomaly detection without relying on heavy ML frameworks.
- ▪The author faced difficulties with existing tools that required cloud resources or high RAM, prompting a need for a different approach.
- ▪Traditional observability tools are not suitable for ARM edge devices due to their resource constraints and connectivity issues.
- ▪The author advocates for using eBPF to filter network events directly in the kernel, allowing for efficient data collection with minimal overhead.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 2175334) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Mohamed Zrouga Posted on May 25 I'm not an ML engineer. I built one anyway. #machinelearning #linux #security #go Not because I wanted to — but because every tool I tried on ARM edge devices either needed the cloud, needed a GPU, or needed more RAM than the service it was supposed to be watching. So this post isn't really about Cerberus.
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