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What is explainable AI? The key to closing the AI confidence gap

Dynatrace

More transparency means a better understanding of the technology being used, better troubleshooting, and more opportunities to fine-tune an organization’s tools. To some, it’s a design methodology — a foundational pillar of the AI model development process. These tools can turn data into actionable insights.

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Evolving from Rule-based Classifier: Machine Learning Powered Auto Remediation in Netflix Data…

The Netflix TechBlog

Operational automation–including but not limited to, auto diagnosis, auto remediation, auto configuration, auto tuning, auto scaling, auto debugging, and auto testing–is key to the success of modern data platforms. We have also noted a great potential for further improvement by model tuning (see the section of Rollout in Production).

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Zero Configuration Service Mesh with On-Demand Cluster Discovery

The Netflix TechBlog

Today we have a wealth of tools, both OSS and commercial, all designed for cloud-native environments. To improve availability, we designed systems where components could fail separately and avoid single points of failure. Eureka and Ribbon presented a simple but powerful interface, which made adopting them easy.

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Efficient SLO event integration powers successful AIOps

Dynatrace

For instance, consider how fine-tuned failure rate detection can provide insights for comprehensive understanding. Please refer to How to fine-tune failure detection (dynatrace.com) for further information. Every problem identified through Dynatrace Davis® AI indicates an issue with potential user impact.

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MongoDB Best Practices: Security, Data Modeling, & Schema Design

Percona

The main objective of this post is to share my experience over the past years tuning MongoDB and centralize the diverse sources that I crossed in this journey in a unique place. The swap issue is explained in the excellent article by Jeremy Cole at the Swap Insanity and NUMA Architecture. mongodb-sysctl.conf – if /etc/sysctl.d

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AVA Discovery View: Surfacing Authentic Moments

The Netflix TechBlog

The tool provides an efficient way for creatives (photo editors, artwork designers, etc.) With the AVA Discovery View, all the prominent characters of the title and their best possible shots are presented to the creatives. Each algorithm needed a process of evaluation and tuning to get great results in AVA Discovery View.

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Rebuilding Netflix Video Processing Pipeline with Microservices

The Netflix TechBlog

This architecture shift greatly reduced the processing latency and increased system resiliency. When Reloaded was designed, we focused on a single use case: converting high-quality media files (also known as mezzanines) received from studios into compressed assets for Netflix streaming.