メタのザッカーバーグＣＥＯ、新たな機関設立－ＡＩ製品で助言受ける
米メタ・プラットフォームズのマーク・ザッカーバーグ最高経営責任者（ＣＥＯ）は、新たな製品に関する助言機関を設けた。メタ経営陣と定期的に会合を開き、同社の人工知能（ＡＩ）や技術の向上について助言するグループとなる。 「メタ・アドバイザリー・グループ」と呼ばれる同機関には、ストライプの共同創業者パトリック・コリソンＣＥＯ、ギットハブのナット・フリードマン元ＣＥＯ、ショッピファイのトビアス・リュトケＣＥＯ、投資家でマイクロソフト元幹部のチャーリー・ソングハースト氏ら４人がメンバーとして名を連ねる。メタの広報担当者によると、全員に対して報酬は支払われない。 広報担当によれば、メタ・アドバイザリー・グループは取締役会とは異なり、株主によるメンバーの選任やメタに対する忠実義務はない。「技術面での向上やイノベーション、戦略的な成長機会に関する見識と助言を提供する役割を担う」という。 原題：Meta’s
Syntax - Tailwind CSS Documentation Template
Writing documentation is enough to make you rip your hair out without worrying about the design. Let us help you keep a few strands. Syntax is a beautiful documentation template built with Tailwind CSS and Next.js, designed and built by the Tailwind CSS team. Powered by Markdoc, the new Markdown-based authoring framework from Stripe, it's now easier than ever to focus on writing great documentatio
Index
Lecture 11: Hiring and Culture, Part II Patrick Collison Follow @patrickc John Collison Follow @collision Ben Silbermann Follow @8en View the annotated transcript, and add annotations of your own, on Genius, or directly at the bottom of this page! Discuss this lecture!
Fast and flexible observability with canonical log lines
Logging is one of the oldest and most ubiquitous patterns in computing. Key to gaining insight into problems ranging from basic failures in test environments to the most tangled problems in production, it’s common practice across all software stacks and all types of infrastructure, and has been for decades. Although logs are powerful and flexible, their sheer volume often makes it impractical to e
APIs as infrastructure: future-proofing Stripe with versioning
When it comes to APIs, change isn’t popular. While software developers are used to iterating quickly and often, API developers lose that flexibility as soon as even one user starts consuming their interface. Many of us are familiar with how the Unix operating system evolved. In 1994, The Unix-Haters Handbook was published containing a long list of missives about the software—everything from overly
Effectively using AWS Reserved Instances
Stripe uses Amazon Web Services to power our infrastructure. With AWS, we can dynamically scale our fleet of servers in real-time. This elasticity enables us to reliably serve a rapidly growing user base and scale along with their businesses. We use AWS Reserved Instances, which allow us to predictably forecast our cloud spend given a dynamic fleet with rapidly changing compute requirements. One o
Stripe: Radar 技術ガイド
このところの E-コマースの拡大、加速に伴って、オンライン決済の不正も増加しています。不正使用がビジネスに与える直接的な損失は世界全体で年間 200 億ドルに達しますが、不正使用による損失が発生するごとに運用コスト、ネットワーク手数料、顧客の解約が増加するため、実際の損失総額はこれよりずっと大きくなります。 不正使用には高額な費用が伴うだけでなく、巧妙な不正使用者が弱点を悪用する新しい方法を常に探しているため、不正使用対策が困難になっています。そのため、Stripe は、Stripe プラットフォームに完全に実装された機械学習ベースの不正防止ソリューション、Stripe Radar を構築しました。Radar の機械学習では、毎年、Stripe ネットワーク全体で処理される数千億ドルの決済から得られるデータを活用し、最新の傾向に迅速に適応することで、お客様が不正使用を増やすことなく成長を実
Learning to operate Kubernetes reliably
We recently built a distributed cron job scheduling system on top of Kubernetes, an exciting new platform for container orchestration. Kubernetes is very popular right now and makes a lot of exciting promises: one of the most exciting is that engineers don’t need to know or care what machines their applications run on. Distributed systems are really hard, and managing services on distributed syste
Just-So Stories for AI: Explaining Black Box Predictions
As machine learning techniques become more powerful, humans and companies are offloading more and more ethical decisions to ML models. Which person should get a loan? Where should I direct my time and attention? Algorithms often outperform humans, so we cede our control happily and love the extra time and leverage this gives us. There's lurking danger here. Many of the most successful machine lear
Rob Story - Machine Learning Infrastructure at Stripe: Bridging from Python JVM
Description Machine learning at Stripe has a foundation built on Python and the PyData stack, with scikit-learn and pandas continuing to be core components of an ML pipeline that feeds a production system written in Scala. This talk will cover the ML Infra team’s work to bridge the serialization and scoring gap between Python and the JVM, as well as how ML Engineers ship models to production. Abs
scale.bythebay.io: Oscar Boykin, How to Elm-ify Your ML
Scale By the Bay 2019 is held on November 13-15 in sunny Oakland, California, on the shores of Lake Merritt: https://scale.bythebay.io. Join us! ----- Functional Reactive Programming for Feature Engineering in Machine Learning I will discuss the system we built at Stripe to enable modelers to quickly define complex features and have them for training and also in realtime for scoring.
Stripe: 募集中の職種
当社は大規模に事業を行っていますが、機会の規模に比べると組織の規模はまだ十分ではありません。Stripe の採用情報にご興味のある方は、現在グローバルチームで募集中の職種をご覧ください。
リリース、障害情報などのサービスのお知らせ
最新の人気エントリーの配信
j次のブックマーク
k前のブックマーク
lあとで読む
eコメント一覧を開く
oページを開く
A powerful, flexible, Markdown-based authoring framework
How Stripe Built a Writing Culture - Knock Down Silos by Slab
「これは革命」とまでは言わないけれど、Webサービスへのサブスク導入がめちゃめちゃ楽なのでぜひ知って欲しいStripeのFirebase Extensions - nabettu
The AWS Billing Machine and Optimizing Cloud Costs | USENIX
SF Scala: Oscar Boykin, Reproducible Machine Learning with Functional Programming - YouTube
Login to Meetup | Meetup
Supporting Hypothesis
The Unstoppable API Era - Keen