Remove 2007 Remove Development Remove Efficiency Remove Innovation
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Improving our video encodes for legacy devices

The Netflix TechBlog

we announced our intention to stream video over 13 years ago, in January 2007?—?and and thus fall back to less efficient encode families. Since then, we have applied innovations such as shot-based encoding and newer codecs to deploy more efficient encode families. 264/AVC Main profile family.

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Happy 15th, Tasktop! Happy 5th, Tasktop Hub!

Tasktop

January 17, 2007: Dr. Mik Kersten, Dr. Gail Murphy and Robert Elves founded Tasktop. Just to put it in context, here are some other highlights from 2007: Apple iPhone was announced and launched. We focused on helping developers context switch less and drive productivity. We’ve had some laughs and a lot of fun getting here.

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Everyone a Beginner?

The Agile Manager

There is a difference between development companies (or divisions within companies) and operating companies. A development company is in the invention or innovation business. There is a high tolerance for operational inconsistency from a development company. There is low tolerance for operational inconsistency.

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Rebuilding Netflix Video Processing Pipeline with Microservices

The Netflix TechBlog

The Netflix video processing pipeline went live with the launch of our streaming service in 2007. We rolled out encoding innovations such as per-title and per-shot optimizations, which provided significant quality-of-experience (QoE) improvement to Netflix members. This introductory blog focuses on an overview of our journey.

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Egnyte Architecture: Lessons learned in building and scaling a multi petabyte content platform

High Scalability

Egnyte is a secure Content Collaboration and Data Governance platform, founded in 2007 when Google drive wasn't born and AWS S3 was cost-prohibitive. In 2007, businesses had started to become more distributed; customers were using multiple devices to access their files and there was a need to make this experience as smooth as possible.

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Scaling Media Machine Learning at Netflix

The Netflix TechBlog

We have been leveraging machine learning (ML) models to personalize artwork and to help our creatives create promotional content efficiently. In order to satisfy this requirement, ML practitioners had to develop bespoke triggering and orchestration components per pipeline.

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Data Mining Problems in Retail

Highly Scalable

More specifically, the article was inspired by three major case studies from Albert Heijn [KOK07], the largest supermarket chain in the Netherlands, Zara [CA12], an international apparel retailer, and RueLaLa [JH14], an innovative online fashion retailer. At the same time, we avoid academic results with little or no empirical support.

Retail 152