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

DZone

Caching is a critical technique for optimizing application performance by temporarily storing frequently accessed data, allowing for faster retrieval during subsequent requests. Multi-layered caching involves using multiple levels of cache to store and retrieve data.

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Supporting Diverse ML Systems at Netflix

The Netflix TechBlog

The Machine Learning Platform (MLP) team at Netflix provides an entire ecosystem of tools around Metaflow , an open source machine learning infrastructure framework we started, to empower data scientists and machine learning practitioners to build and manage a variety of ML systems. For many of our applications, model explainability matters.

Systems 226
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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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Dynatrace accelerates business transformation with new AI observability solution

Dynatrace

GenAI is prone to erratic behavior due to unforeseen data scenarios or underlying system issues. For example, a Stanford University and UC Berkeley team noted in a research study that ChatGPT behavior deteriorates over time. Failure to provide timely and accurate answers erodes user trust, hinders adoption, and harms retention.

Cache 201
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Best practices and key metrics for improving mobile app performance

Dynatrace

From the customer perspective, mobile devices have become the singular touchpoint between businesses and users, for example, the new storefront, office, and customer support line. For example, an app that does not crash often but is frequently slow from a user’s perspective is providing a poor user experience.

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Migrating Critical Traffic At Scale with No Downtime?—?Part 1

The Netflix TechBlog

Behind the scenes, a myriad of systems and services are involved in orchestrating the product experience. These backend systems are consistently being evolved and optimized to meet and exceed customer and product expectations. It provides a good read on the availability and latency ranges under different production conditions.

Traffic 339
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Improved Alerting with Atlas Streaming Eval

The Netflix TechBlog

Engineers want their alerting system to be realtime, reliable, and actionable. A few years ago, we were paged by our SRE team due to our Metrics Alerting System falling behind — critical application health alerts reached engineers 45 minutes late! In other words, false positives are bad but false negatives are the absolute worst!

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