Remove tag algorithms
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Scalable Annotation Service?—?Marken

The Netflix TechBlog

Annotations Sometimes people describe annotations as tags but that is a limited definition. We have several ML algorithms which scan Netflix media assets (images and videos) and create very interesting data for example identifying characters in frames or identifying match cuts. algorithmVersion (String) — version of the ML algorithm.

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New Series: Creating Media with Machine Learning

The Netflix TechBlog

With media-focused ML algorithms, we’ve brought science and art together to revolutionize how content is made. These timecode tags enable efficient discovery, freeing our creators from hours of categorizing footage so they can focus on creative decisions instead.

Media 239
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Machine Learning for Fraud Detection in Streaming Services

The Netflix TechBlog

Even though such techniques can scale security solutions proportional to the service size, they bring their own set of challenges such as requiring labeled data samples, defining effective features, and finding appropriate algorithms. Based on this reasoning, we tag all the accounts that acquire licenses very quickly as anomalous.

C++ 312
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Detecting Speech and Music in Audio Content

The Netflix TechBlog

Similarly, algorithms for dialogue intelligibility, spoken-language-identification and speech-transcription are only applied to audio regions where there is measured speech. Special thanks to the entire Audio Algorithms team, as well as Amir Ziai , Anna Pulido , and Angie Pollema. Pre-trained models for each conducted experiment.

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Measuring the importance of data quality to causal AI success

Dynatrace

Another common impediment is manual data tagging and handling, an error-prone process that teams should minimize. Human involvement should be limited to verifying the features or attributes machine learning algorithms use to make predictions or decisions.

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Enhanced root cause analysis using events

Dynatrace

For example, Dynatrace organizes entities into management zones and can tag them with important information, such as the owner and environment. When Davis is aware of these items, it can consider them using its root-cause detection algorithms. Tag your host with demo: cpu_stress. Who needs to be alerted? data-raw '{.

DevOps 181
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Measure What You Impact, Not What You Influence

CSS Wizardry

Working this way allows us to measure only the things we’re actively modifying, and make sure we’re headed in the right direction. If you aren’t already, you should totally make User Timings a part of your day-to-day workflow. On a similar note, I am obsessed with head. Like, obsessed. It’s vital to measure what you impact, not what you influence.