Remove Algorithm Remove Systems Remove Utilities
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Interesting startup idea: benchmarking cloud platform pricing

The Pragmatic Engineer

The name comes from the concept of “spare cores:” machines currently unused, which can be reclaimed at any time, that cloud providers tend to offer at a steep discount to keep server utilization high. Spare Cores attempts to make it easier to compare prices across cloud providers. Source: Spare Cores. Tech stack.

Cloud 277
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Taming the tail utilization of ads inference at Meta scale

Engineering at Meta

Tail utilization is a significant system issue and a major factor in overload-related failures and low compute utilization. The tail utilization optimizations at Meta have had a profound impact on model serving capacity footprint and reliability. Why is tail utilization a problem?

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Movie Recommendation System: Definition, Strategies, Usecase

Knowledge Hut

Not only could this recommendation system save time browsing through lists of movies, it can also give more personalized results so users don’t feel overwhelmed by too many options. What are Movie Recommendation Systems? Recommender systems have two main categories: content-based & collaborative filtering.

Systems 98
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Enhancing Distributed System Load Shedding with TCP Congestion Control Algorithm

Zalando Engineering

But our system is event driven, all requests we process are delivered as events via Nakadi. We know if our system runs within its normal limits that we meet our SLOs. If we would control the ingestion of message requests into our system we would be able to process the task in a timely manner.

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An educational side project

The Pragmatic Engineer

Juraj included system monitoring parts which monitor the server’s capacity he runs the app on: The monitoring page on the Rides app And it doesn’t end here. Juraj created a systems design explainer on how he built this project, and the technologies used: The systems design diagram for the Rides application The app uses: Node.js

Education 363
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Scaling the Instagram Explore recommendations system

Engineering at Meta

Explore is one of the largest recommendation systems on Instagram. Using more advanced machine learning models, like Two Towers neural networks, we’ve been able to make the Explore recommendation system even more scalable and flexible. locally popular media), which further contributes to system scalability.

Systems 98
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Arcadia: An end-to-end AI system performance simulator

Engineering at Meta

We’re introducing Arcadia, Meta’s unified system that simulates the compute, memory, and network performance of AI training clusters. We need a systemized source of truth that can simulate various performance factors across compute, storage, and network collectively. For instance, the AI Research SuperCluster for AI research.

Systems 109