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Inside Facebook’s video delivery system

Engineering at Meta

Were explaining the end-to-end systems the Facebook app leverages to deliver relevant content to people. At Facebooks scale, the systems built to support and overcome these challenges require extensive trade-off analyses, focused optimizations, and architecture built to allow our engineers to push for the same user and business outcomes.

Systems 73
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The Future of Reliable Data + AI—Observing the Data, System, Code, and Model

Monte Carlo

GitHub copilot can even code alongside you like your own pocket-sized Steve Wozniak. Table of Contents Understanding How Data + AI Can Break Data System Code Model Data + AI observability must cover inputs and outputs it is all or nothing Understanding How Data + AI Can Break Data + AI applications are complex.

Coding 52
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Interesting startup idea: benchmarking cloud platform pricing

The Pragmatic Engineer

We recently covered how CockroachDB joins the trend of moving from open source to proprietary and why Oxide decided to keep using it with self-support , regardless Web hosting:  Netlify : chosen thanks to their super smooth preview system with SSR support. Internal comms: Chat: Slack Coordination / project management: Linear 3.

Cloud 326
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An Easy Introduction to Machine Learning Recommender Systems

KDnuggets

Recommender systems are an important class of machine learning algorithms that offer "relevant" suggestions to users. Categorized as either collaborative filtering or a content-based system, check out how these approaches work along with implementations to follow from example code.

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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 364
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Bring Your Own Algorithm to Anomaly Detection

Pinterest Engineering

Warden started off as a Java Thrift service built around the EGADs open-source library, which contains Java implementations of various time-series anomaly detection algorithms. They found the existing selection of anomaly detection algorithms in EGADs to be limiting. Each job is load-balanced to a node in the Warden cluster.

Algorithm 111
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Title Launch Observability at Netflix Scale

Netflix Tech

In this case, the main stakeholders are: - Title Launch Operators Role: Responsible for setting up the title and its metadata into our systems. In this context, were focused on developing systems that ensure successful title launches, build trust between content creators and our brand, and reduce engineering operational overhead.