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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.

Systems 226
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Front-End: Cache Strategies You Should Know

DZone

Caches are very useful software components that all engineers must know. It is a transversal component that applies to all the tech areas and architecture layers such as operating systems, data platforms, backend, frontend, and other components. What Is a Cache?

Cache 141
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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 204
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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! It opens doors to support more exciting use-cases.

Storage 288
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Designing Instagram

High Scalability

Machine Learning Engineer at Amazon and has led several machine-learning initiatives across the Amazon ecosystem. 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. Fetching User Feed.

Design 334
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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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AI-driven analysis of Spring Micrometer metrics in context, with typology at scale

Dynatrace

Spring Boot 2 uses Micrometer as its default application metrics collector and automatically registers metrics for a wide variety of technologies, like JVM, CPU Usage, Spring MVC, and WebFlux request latencies, cache utilization, data source utilization, Rabbit MQ connection factories, and more. That’s a large amount of data to handle.

Metrics 207