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

The Netflix TechBlog

Evolving to Auto Remediation: Service Architecture Methodology To address the above-mentioned challenges, our basic methodology is to integrate the rule-based classifier with an ML service to generate recommendations, and use a configuration service to apply the recommendations automatically: Generating recommendations.

Tuning 210
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What is IT automation?

Dynatrace

As organizations continue to adopt multicloud strategies, the complexity of these environments grows, increasing the need to automate cloud engineering operations to ensure organizations can enforce their policies and architecture principles. AI that is based on machine learning needs to be trained.

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Incremental Processing using Netflix Maestro and Apache Iceberg

The Netflix TechBlog

IPS enables users to continue to use the data processing patterns with minimal changes. Introduction Netflix relies on data to power its business in all phases. As our business scales globally, the demand for data is growing and the needs for scalable low latency incremental processing begin to emerge. past 3 hours or 10 days).

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Education 105
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Education 102
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Education 102