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The detailed changelog is published on GitHub. Version 0.13.0.2: reintroduce ability to specify different version info for each of the frozen binaries fix a bug in MemoryModule.c:PerformBaseRelocation fix missing initialization settings for the runtime Python interpreter add support for setuptools v72 fix the matplotlib, numpy, and scipy hooks fix py2exe wheels build with setuptools v70.0.0 Versio
説明 pyenv 等でpython3, gcloudをインストールした環境では、gcloud実行時に以下のようなエラーが出ることがある。 $ pyenv local 3.7.4 $ gcloud pyenv: python2: command not found The `python2' command exists in these Python versions: 2.7.17 gcloudがpython2でしか動作しないのかというとそうではなく、 ドキュメントにはGoogle Cloud SDK version 274.0.0以降であればpython3.5以上で動作する(GA)と書いてある。 https://cloud.google.com/sdk/docs/?hl=en As of Cloud SDK version 274.0.0, the gcloud CLI has GA
Infrastructure for Contextual Bandits and Reinforcement Learning — theme of the ML Platform meetup hosted at Netflix, Los Gatos on Sep 12, 2019. Contextual and Multi-armed Bandits enable faster and adaptive alternatives to traditional A/B Testing. They enable rapid learning and better decision-making for product rollouts. Broadly speaking, these approaches can be seen as a stepping stone to full-o
By Cody Rioux, Daniel Jacobson, Jeff Chao, Neeraj Joshi, Nick Mahilani, Piyush Goyal, Prashanth Ramdas, Zhenzhong Xu Today we’re excited to announce that we’re open sourcing Mantis, a platform that helps Netflix engineers better understand the behavior of their applications to ensure the highest quality experience for our members. We believe the challenges we face here at Netflix are not necessari
社内ではこういうおすすめをしてますね(文字数多いのでスクショで...) pic.twitter.com/uzqCh6zubs— 柴崎優季 (@shiba_yu36) 2020年7月7日 こういうツイートして、そういえば社内でメンターを初めて経験する人にオススメしている書籍たちを外部に公開してないなと思ったので紹介してみます。 メンタリングのスキルを学習する時のキーワードは「コーチング」と考えていて、以下の書籍を推薦しています。上から順におすすめ順になっています。この推薦は網羅的にコーチングを学べると言うより、初めての人でもとっつきやすく読みやすいものであることを意識して選んでいます。また、メンタリングを始めるだけなら、書籍の全部分を読む必要はなく、どこまで読んでおくと良いかも書いています。 エンジニアリング組織論への招待 ザ・コーチ コーチングの基本 新1分間マネジャー エンジニアリング組
Toby Mao, Sri Sri Perangur, Colin McFarland Another day, another custom script to analyze an A/B test. Maybe you’ve done this before and have an old script lying around. If it’s new, it’s probably going to take some time to set up, right? Not at Netflix. ABlaze: The standard view of analyses in the XP UISuppose you’re running a new video encoding test and theorize that the two new encodes should r
By: Di Lin, Girish Lingappa, Jitender Aswani Imagine yourself in the role of a data-inspired decision maker staring at a metric on a dashboard about to make a critical business decision but pausing to ask a question — “Can I run a check myself to understand what data is behind this metric?” Now, imagine yourself in the role of a software engineer responsible for a micro-service which publishes dat
By Ammar Khaku IntroductionIn a microservice architecture such as Netflix’s, propagating datasets from a single source to multiple downstream destinations can be challenging. These datasets can represent anything from service configuration to the results of a batch job, are often needed in-memory to optimize access and must be updated as they change over time. One example displaying the need for d
by Artem Shtatnov and Ravi Srinivas Ranganathan Almost a year ago we described our learnings from adopting GraphQL on the Netflix Marketing Tech team. We have a lot more to share since then! There are plenty of existing resources describing how to express a search query in GraphQL and paginate the results. This post looks at the other side of search: how to index data and make it searchable. Speci
Vega - A Visualization Grammar. Vega is a visualization grammar, a declarative format for creating, saving, and sharing interactive visualization designs. With Vega, you can describe the visual appearance and interactive behavior of a visualization in a JSON format, and generate web-based views using Canvas or SVG.
We are pleased to announce the open-source launch of Polynote: a new, polyglot notebook with first-class Scala support, Apache Spark integration, multi-language interoperability including Scala, Python, and SQL, as-you-type autocomplete, and more. Polynote provides data scientists and machine learning researchers with a notebook environment that allows them the freedom to seamlessly integrate our
Netflix has a program in our Information Security department for quantifying the risk of deliberate (attacker-driven) and accidental losses. This program started on the Detection Engineering team with a home-grown Python library called riskquant, which we’ve released as open source for you to use (and contribute to). Since that library was written, we have hired two amazing full-time Risk Engineer
By Hank Jacobs, Senior Site Reliability Engineer on CORE We’re privileged to be in the business of bringing joy to our customers at Netflix. Whether it’s a compelling new series or an innovative product feature, we strive to provide a best-in-class service that people love and can enjoy anytime, anywhere. A key underpinning to keeping our customers happy and streaming is a strong focus on reliabil
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