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Data Engineers of Netflix?—?Interview with Pallavi Phadnis

The Netflix TechBlog

Data Engineers of Netflix?—?Interview Interview with Pallavi Phadnis This post is part of our “ Data Engineers of Netflix ” series, where our very own data engineers talk about their journeys to Data Engineering @ Netflix. Pallavi Phadnis is a Senior Software Engineer at Netflix.

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Supporting Diverse ML Systems at Netflix

The Netflix TechBlog

Importantly, all the use cases were engineered by practitioners themselves. These integrations are implemented through Metaflow’s extension mechanism which is publicly available but subject to change, and hence not a part of Metaflow’s stable API yet. We have developed an officially supported pattern to cover such use cases.

Systems 226
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Spice up your Analytics: Amazon QuickSight Now Generally Available in N. Virginia, Oregon, and Ireland.

All Things Distributed

Today, I am very happy to announce that QuickSight is now generally available in the N. When we announced QuickSight last year, we set out to help all customers—regardless of their technical skills—make sense out of their ever-growing data. Put simply, data is not always readily available and accessible to organizational end users.

Analytics 152
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Experimentation is a major focus of Data Science across Netflix

The Netflix TechBlog

Curious to learn about what it’s like to be a Data Engineer at Netflix? Hear directly from Samuel Setegne , Dhevi Rajendran , Kevin Wylie , and Pallavi Phadnis in our “Data Engineers of Netflix” interview series. In such cases, we analyze aggregate level data using synthetic controls and structural models.

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5 key areas for tech leaders to watch in 2020

O'Reilly

This year’s growth in Python usage was buoyed by its increasing popularity among data scientists and machine learning (ML) and artificial intelligence (AI) engineers. Software architecture, infrastructure, and operations are each changing rapidly. Python libraries are no less useful for manipulating or engineering data, too.).

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How Data Inspires Building a Scalable, Resilient and Secure Cloud Infrastructure At Netflix

The Netflix TechBlog

As a micro-service owner, a Netflix engineer is responsible for its innovation as well as its operation, which includes making sure the service is reliable, secure, efficient and performant. In the Efficiency space, our data teams focus on transparency and optimization.

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Formulating ‘Out of Memory Kill’ Prediction on the Netflix App as a Machine Learning Problem

The Netflix TechBlog

More importantly, the low resource availability or “out of memory” scenario is one of the common reasons for crashes/kills. We at Netflix, as a streaming service running on millions of devices, have a tremendous amount of data about device capabilities/characteristics and runtime data in our big data platform.

Big Data 179