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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 226
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Software-Defined Networking in Distributed Systems: Transforming Data Centers and Cloud Computing Environments

DZone

In the changing world of data centers and cloud computing, the desire for efficient, flexible, and scalable networking solutions has resulted in the broad use of Software-Defined Networking (SDN). Traditional networking models have a tightly integrated control plane and data plane within network devices.

Network 169
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Trace, diagnose, resolve: Introducing the Infrastructure & Operations app for streamlined troubleshooting

Dynatrace

Infrastructure and operations teams must maintain infrastructure health for IT environments. The complex interconnections in cloud-based systems make it crucial to always have a topological overview to understand dependencies. This allows you to pinpoint troubled data centers at a glance.

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Building Netflix’s Distributed Tracing Infrastructure

The Netflix TechBlog

which is difficult when troubleshooting distributed systems. Now let’s look at how we designed the tracing infrastructure that powers Edgar. This insight led us to build Edgar: a distributed tracing infrastructure and user experience. Stream Processing: to sample or not to sample trace data?

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Dynatrace EdgeConnect securely connects your local systems to Dynatrace SaaS

Dynatrace

EdgeConnect provides a secure bridge for SaaS-heavy companies like Dynatrace, which hosts numerous systems and data behind VPNs. EdgeConnect facilitates seamless interaction, ensuring data security and operational efficiency. EdgeConnect is designed to forward HTTP(s) requests exclusively, ensuring secure data transmission.

Systems 225
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Measuring the importance of data quality to causal AI success

Dynatrace

Traditional analytics and AI systems rely on statistical models to correlate events with possible causes. While this approach can be effective if the model is trained with a large amount of data, even in the best-case scenarios, it amounts to an informed guess, rather than a certainty. But to be successful, data quality is critical.

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What is predictive AI? How this data-driven technique gives foresight to IT teams

Dynatrace

Technology and operations teams work to ensure that applications and digital systems work seamlessly and securely. They handle complex infrastructure, maintain service availability, and respond swiftly to incidents. Through predictive analytics, SREs and DevOps engineers can accurately forecast resource needs based on historical data.