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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. the retry success probability) and compute cost efficiency (i.e., Multi-objective optimizations.

Tuning 210
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What is IT automation?

Dynatrace

Ultimately, IT automation can deliver consistency, efficiency, and better business outcomes for modern enterprises. Automating IT practices offers enterprises faster data centers and cloud operations, as well as increased flexibility and accuracy. AI that is based on machine learning needs to be trained. Read eBook now!

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Incremental Processing using Netflix Maestro and Apache Iceberg

The Netflix TechBlog

We will show how we are building a clean and efficient incremental processing solution (IPS) by using Netflix Maestro and Apache Iceberg. IPS provides the incremental processing support with data accuracy, data freshness, and backfill for users and addresses many of the challenges in workflows. past 3 hours or 10 days).

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Orchestrating Data/ML Workflows at Scale With Netflix Maestro

The Netflix TechBlog

by Jun He , Akash Dwivedi , Natallia Dzenisenka , Snehal Chennuru , Praneeth Yenugutala , Pawan Dixit At Netflix, Data and Machine Learning (ML) pipelines are widely used and have become central for the business, representing diverse use cases that go beyond recommendations, predictions and data transformations.

Java 202
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Python at Netflix

The Netflix TechBlog

Demand Engineering Demand Engineering is responsible for Regional Failovers , Traffic Distribution, Capacity Operations and Fleet Efficiency of the Netflix cloud. Our Infrastructure Security team leverages Python to help with IAM permission tuning using Repokid. We also use Python to detect sensitive data using Lanius.

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Structural Evolutions in Data

O'Reilly

Each time, the underlying implementation changed a bit while still staying true to the larger phenomenon of “Analyzing Data for Fun and Profit.” ” They weren’t quite sure what this “data” substance was, but they’d convinced themselves that they had tons of it that they could monetize.