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Architectural Insights: Designing Efficient Multi-Layered Caching With Instagram Example

DZone

Leveraging this hierarchical structure can significantly reduce latency and improve overall performance.

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Timestone: Netflix’s High-Throughput, Low-Latency Priority Queueing System with Built-in Support…

The Netflix TechBlog

Timestone: Netflix’s High-Throughput, Low-Latency Priority Queueing System with Built-in Support for Non-Parallelizable Workloads by Kostas Christidis Introduction Timestone is a high-throughput, low-latency priority queueing system we built in-house to support the needs of Cosmos , our media encoding platform. Over the past 2.5

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Crucial Redis Monitoring Metrics You Must Watch

Scalegrid

Key Takeaways Critical performance indicators such as latency, CPU usage, memory utilization, hit rate, and number of connected clients/slaves/evictions must be monitored to maintain Redis’s high throughput and low latency capabilities. These essential data points heavily influence both stability and efficiency within the system.

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Implementing AWS well-architected pillars with automated workflows

Dynatrace

This is a set of best practices and guidelines that help you design and operate reliable, secure, efficient, cost-effective, and sustainable systems in the cloud. The framework comprises six pillars: Operational Excellence, Security, Reliability, Performance Efficiency, Cost Optimization, and Sustainability.

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

The Netflix TechBlog

For example, we invest in next-generation, royalty-free codecs and sophisticated video encoding optimizations. Video downscaling is the most pertinent example herein, which tailors our encoding to screen resolutions of different devices and optimizes picture quality under varying network conditions. A visual example is shown below.

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Dynatrace accelerates business transformation with new AI observability solution

Dynatrace

For example, a Stanford University and UC Berkeley team noted in a research study that ChatGPT behavior deteriorates over time. Using the example of a chatbot, once the user submits a natural language prompt, RAG summarizes that prompt using semantic data. Consequently, AI model drift and hallucinations emerge as primary concerns.

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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. Our example dashboard below visualizes OpenAI token consumption. This includes OpenAI as well as Azure OpenAI services, such as GPT-3, Codex, DALL-E, or ChatGPT.