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Scaling Media Machine Learning at Netflix

The Netflix TechBlog

We have been leveraging machine learning (ML) models to personalize artwork and to help our creatives create promotional content efficiently. Our goal in building a media-focused ML infrastructure is to reduce the time from ideation to productization for our media ML practitioners.

Media 299
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The top eight DevSecOps trends in 2022

Dynatrace

Increased adoption of Infrastructure as code (IaC). IaC, or software intelligence as code , codifies and manages IT infrastructure in software, rather than in hardware. Infrastructure as code is also known as software-defined infrastructure, or software intelligence as code.

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Six causes of major software outages–And how to avoid them

Dynatrace

From business operations to personal communication, the reliance on software and cloud infrastructure is only increasing. Employee training in cybersecurity best practices and maintaining up-to-date software and systems are also crucial. Outages can disrupt services, cause financial losses, and damage brand reputations.

Software 260
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For your eyes only: improving Netflix video quality with neural networks

The Netflix TechBlog

While conventional video codecs remain prevalent, NN-based video encoding tools are flourishing and closing the performance gap in terms of compression efficiency. During training, our goal is to generate the best downsampled representation such that, after upscaling, the mean squared error is minimized.

Network 302
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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 238
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Why growing AI adoption requires an AI observability strategy

Dynatrace

As organizations turn to artificial intelligence for operational efficiency and product innovation in multicloud environments, they have to balance the benefits with skyrocketing costs associated with AI. The good news is AI-augmented applications can make organizations massively more productive and efficient.

Strategy 234
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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 215