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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. ETL workflows), as well as downstream (e.g.

Systems 226
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Optimize your environment: Unveiling Dynatrace Hyper-V extension for enhanced performance and efficient troubleshooting

Dynatrace

Microsoft Hyper-V is a virtualization platform that manages virtual machines (VMs) on Windows-based systems. It enables multiple operating systems to run simultaneously on the same physical hardware and integrates closely with Windows-hosted services. This leads to a more efficient and streamlined experience for users.

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What is a Distributed Storage System

Scalegrid

A distributed storage system is foundational in today’s data-driven landscape, ensuring data spread over multiple servers is reliable, accessible, and manageable. This guide delves into how these systems work, the challenges they solve, and their essential role in businesses and technology.

Storage 130
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What is serverless computing? Driving efficiency without sacrificing observability

Dynatrace

Traditional computing models rely on virtual or physical machines, where each instance includes a complete operating system, CPU cycles, and memory. There is no need to plan for extra resources, update operating systems, or install frameworks. The provider is essentially your system administrator. What is serverless computing?

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Why applying chaos engineering to data-intensive applications matters

Dynatrace

Stream processing One approach to such a challenging scenario is stream processing, a computing paradigm and software architectural style for data-intensive software systems that emerged to cope with requirements for near real-time processing of massive amounts of data. This significantly increases event latency.

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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.

Metrics 130
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Redis® Monitoring Strategies for 2024

Scalegrid

Identifying key Redis® metrics such as latency, CPU usage, and memory metrics is crucial for effective Redis monitoring. With these essential support systems in place, you can effectively monitor your databases with up-to-date data about their health and functioning status at all times.

Strategy 130