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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.

Systems 226
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Dynatrace accelerates business transformation with new AI observability solution

Dynatrace

Augmenting LLM input in this way reduces apparent knowledge gaps in the training data and limits AI hallucinations. The LLM then synthesizes the retrieved data with the augmented prompt and its internal training data to create a response that can be sent back to the user. million AI server units annually by 2027, consuming 75.4+

Cache 209
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Why growing AI adoption requires an AI observability strategy

Dynatrace

And an O’Reilly Media survey indicated that two-thirds of survey respondents have already adopted generative AI —a form of AI that uses training data to create text, images, code, or other types of content that reflect its users’ natural language queries. AI requires more compute and storage. AI performs frequent data transfers.

Strategy 226
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Service level objectives: 5 SLOs to get started

Dynatrace

While gyms and fitness studios have since reopened, these apps are still extremely popular for squeezing in short home workouts, tracking fitness goals, and receiving personalized training recommendations. Note : you might hear the term latency used instead of response time. Latency primarily focuses on the time spent in transit.

Latency 181
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Rebuilding Netflix Video Processing Pipeline with Microservices

The Netflix TechBlog

This architecture shift greatly reduced the processing latency and increased system resiliency. We expanded pipeline support to serve our studio/content-development use cases, which had different latency and resiliency requirements as compared to the traditional streaming use case. This drove the approach of the “release train”.

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Service level objective examples: 5 SLO examples for faster, more reliable apps

Dynatrace

While gyms and fitness studios have since reopened, these apps are still extremely popular for squeezing in short home workouts, tracking fitness goals, and receiving personalized training recommendations. Note : you might hear the term latency used instead of response time. Latency primarily focuses on the time spent in transit.

Traffic 173
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For your eyes only: improving Netflix video quality with neural networks

The Netflix TechBlog

During training, our goal is to generate the best downsampled representation such that, after upscaling, the mean squared error is minimized. We focus on a robust downscaler that is trained given a conventional upscaler, like bicubic. We focus on a robust downscaler that is trained given a conventional upscaler, like bicubic.

Network 292