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Demystifying Interviewing for Backend Engineers @ Netflix

The Netflix TechBlog

You apply for multiple roles at the same company and proceed through the interview process with each hiring team separately, despite the fact that there is tremendous overlap in the roles. Interviewing can be a daunting endeavor and how companies, and teams, approach the process varies greatly.

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Open-Sourcing Metaflow, a Human-Centric Framework for Data Science

The Netflix TechBlog

mainly because of mundane reasons related to software engineering. We heard many stories about difficulties related to data access and basic data processing. By design, Metaflow is a deceptively simple Python library: Data scientists can structure their workflow as a Directed Acyclic Graph of steps, as depicted above.

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Open-Sourcing Metaflow, a Human-Centric Framework for Data Science

The Netflix TechBlog

mainly because of mundane reasons related to software engineering. We heard many stories about difficulties related to data access and basic data processing. By design, Metaflow is a deceptively simple Python library: Data scientists can structure their workflow as a Directed Acyclic Graph of steps, as depicted above.

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A Day in the Life of… a Software Training Specialist

Tasktop

I Also like to spend a little bit of time stretching and doing light exercises, reading or playing with my daughter before I dive into some work. I enjoy the process of distilling information from various sources and putting it into a format that our trainees can easily digest and comprehend.

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Automating chaos experiments in production

The Morning Paper

This is a fascinating paper from members of Netflix’s Resilience Engineering team describing their chaos engineering initiatives: automated controlled experiments designed to verify hypotheses about how the system should behave under gray failure conditions, and to probe for and flush out any weaknesses. Safeguards.

Latency 77
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Teaching rigorous distributed systems with efficient model checking

The Morning Paper

We would like students to consider the performance characteristics of their systems, and our tests check that their designs attain reasonable run-time performance. Consider the lab exercise to implement Paxos. and used to taking small incremental steps, fail to appreciate the need for some degree of up-front design. (Of

Systems 43
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MLOps and DevOps: Why Data Makes It Different

O'Reilly

This is both frustrating for companies that would prefer making ML an ordinary, fuss-free value-generating function like software engineering, as well as exciting for vendors who see the opportunity to create buzz around a new category of enterprise software. All ML projects are software projects.

DevOps 140