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Building Netflix’s Distributed Tracing Infrastructure

The Netflix TechBlog

Now let’s look at how we designed the tracing infrastructure that powers Edgar. If we had an ID for each streaming session then distributed tracing could easily reconstruct session failure by providing service topology, retry and error tags, and latency measurements for all service calls.

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Why applying chaos engineering to data-intensive applications matters

Dynatrace

Failures can occur unpredictably across various levels, from physical infrastructure to software layers. Stream processing systems, designed for continuous, low-latency processing, demand swift recovery mechanisms to tolerate and mitigate failures effectively. We designed experimental scenarios inspired by chaos engineering.

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Dynatrace accelerates business transformation with new AI observability solution

Dynatrace

Data dependencies and framework intricacies require observing the lifecycle of an AI-powered application end to end, from infrastructure and model performance to semantic caches and workflow orchestration. Estimates show that NVIDIA, a semiconductor manufacturer, could release 1.5 million AI server units annually by 2027, consuming 75.4+

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Dynatrace automatically monitors OpenAI ChatGPT for companies that deliver reliable, cost-effective services powered by generative AI

Dynatrace

A typical design pattern is the use of a semantic search over a domain-specific knowledge base, like internal documentation, to provide the required context in the prompt. With these latency, reliability, and cost measurements in place, your operations team can now define their own OpenAI dashboards and SLOs.

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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.

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Seamlessly Swapping the API backend of the Netflix Android app

The Netflix TechBlog

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. To prepare ourselves for a big change in the tech stack of our endpoint, we decided to track metrics around the time taken to respond to queries.

Latency 241
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Interpreting A/B test results: false negatives and power

The Netflix TechBlog

Subsequent posts will go into more details on experimentation across Netflix, how Netflix has invested in infrastructure to support and scale experimentation, and the importance of the culture of experimentation within Netflix. the difference in metric values between Groups A and B?—?the Simply put, the larger the effect size?—?the

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