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Supporting Diverse ML Systems at Netflix

The Netflix TechBlog

Berg , Romain Cledat , Kayla Seeley , Shashank Srikanth , Chaoying Wang , Darin Yu Netflix uses data science and machine learning across all facets of the company, powering a wide range of business applications from our internal infrastructure and content demand modeling to media understanding. ETL workflows), as well as downstream (e.g.

Systems 226
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Dynatrace accelerates business transformation with new AI observability solution

Dynatrace

GenAI is prone to erratic behavior due to unforeseen data scenarios or underlying system issues. The RAG process begins by summarizing and converting user prompts into queries that are sent to a search platform that uses semantic similarities to find relevant data in vector databases, semantic caches, or other online data sources.

Cache 206
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Best practices and key metrics for improving mobile app performance

Dynatrace

User demographics , such as app version, operating system, location, and device type, can help tailor an app to better meet users’ needs and preferences. By monitoring metrics such as error rates, response times, and network latency, developers can identify trends and potential issues, so they don’t become critical. Issue remediation.

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Predictive CPU isolation of containers at Netflix

The Netflix TechBlog

Because microprocessors are so fast, computer architecture design has evolved towards adding various levels of caching between compute units and the main memory, in order to hide the latency of bringing the bits to the brains. This avoids thrashing caches too much for B and evens out the pressure on the L3 caches of the machine.

Cache 251
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Designing Instagram

High Scalability

The streaming data store makes the system extensible to support other use-cases (e.g. FUN FACT : In this talk , Rodrigo Schmidt, director of engineering at Instagram talks about the different challenges they have faced in scaling the data infrastructure at Instagram. System Components. Fetching User Feed. Optimization.

Design 334
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Seamlessly Swapping the API backend of the Netflix Android app

The Netflix TechBlog

This allows the app to query a list of “paths” in each HTTP request, and get specially formatted JSON (jsonGraph) that we use to cache the data and hydrate the UI. The big difference from the monolith, though, is that this is now a standalone service deployed as a separate “application” (service) in our cloud infrastructure.

Latency 233
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Implementing AWS well-architected pillars with automated workflows

Dynatrace

This is a set of best practices and guidelines that help you design and operate reliable, secure, efficient, cost-effective, and sustainable systems in the cloud. Storing frequently accessed data in faster storage, usually in-memory caching, improves data retrieval speed and overall system performance. Beyond

AWS 249