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Supporting Diverse ML Systems at Netflix

The Netflix TechBlog

The Machine Learning Platform (MLP) team at Netflix provides an entire ecosystem of tools around Metaflow , an open source machine learning infrastructure framework we started, to empower data scientists and machine learning practitioners to build and manage a variety of ML systems. ETL workflows), as well as downstream (e.g.

Systems 226
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Improved Alerting with Atlas Streaming Eval

The Netflix TechBlog

Engineers want their alerting system to be realtime, reliable, and actionable. A few years ago, we were paged by our SRE team due to our Metrics Alerting System falling behind — critical application health alerts reached engineers 45 minutes late! In other words, false positives are bad but false negatives are the absolute worst!

Storage 288
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Migrating Critical Traffic At Scale with No Downtime?—?Part 1

The Netflix TechBlog

Behind the scenes, a myriad of systems and services are involved in orchestrating the product experience. These backend systems are consistently being evolved and optimized to meet and exceed customer and product expectations. It provides a good read on the availability and latency ranges under different production conditions.

Traffic 339
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Designing Instagram

High Scalability

The streaming data store makes the system extensible to support other use-cases (e.g. System Components. The system will comprise of several micro-services each performing a separate task. When a user requests for feed then there will be two parallel threads involved in fetching the user feeds to optimize for latency.

Design 334
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Current status, needs, and challenges in Heterogeneous and Composable Memory from the HCM workshop (HPCA’23)

ACM Sigarch

Introduction Memory systems are evolving into heterogeneous and composable architectures. Heterogeneous and Composable Memory (HCM) offers a feasible solution for terabyte- or petabyte-scale systems, addressing the performance and efficiency demands of emerging big-data applications. The recently announced CXL3.0

Latency 52
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Dynamic Content Vs. Static Content: What Are the Main Differences

IO River

They cache static content and enable lightning-fast delivery around the globe.This symbiosis reduces server load, boosts loading times, and ensures efficient content distribution. Content Delivery Networks (CDNs), web browsers, and proxy servers can store static files in their caches.

Cache 52
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Dynamic Content Vs. Static Content: What Are the Main Differences

IO River

They cache static content and enable lightning-fast delivery around the globe.This symbiosis reduces server load, boosts loading times, and ensures efficient content distribution. Content Delivery Networks (CDNs), web browsers, and proxy servers can store static files in their caches.

Cache 52