Recommendations The journey to build an explainable AI-driven recommendation system to help scale sales efficiency across LinkedIn Authored byJilei Yang Staff Software Engineer, Machine Learning at LinkedIn | PhD in Statistics April 6, 2022 Co-authors: Jilei Yang, Parvez Ahammmad, Fangfang Tan, Rodrigo Aramayo, Suvendu Jena, Jessica Li At LinkedIn, we have the opportunity to work with many differe
EngineeringPartitioning GitHub’s relational databases to handle scaleIn 2019, to meet GitHub's growth and availability challenges, we set a plan in motion to improve our tooling and ability to partition relational databases. More than 10 years ago, GitHub.com started out like many other web applications of that time—built on Ruby on Rails, with a single MySQL database to store most of its data. Ov
AWS News Blog Introducing Amazon Redshift Serverless – Run Analytics At Any Scale Without Having to Manage Data Warehouse Infrastructure We’re seeing the use of data analytics expanding among new audiences within organizations, for example with users like developers and line of business analysts who don’t have the expertise or the time to manage a traditional data warehouse. Also, some customers h
Opens in a new windowOpens an external siteOpens an external site in a new window Shopify is continuing to invest on Ruby on Rails at scale. We’ve taken that further recently by funding high-profile academics to focus their work towards Ruby and the needs of the Ruby community. Over the past year we have given nearly half a million dollars in gifts to influential researchers that we trust to make
Deep Learning (DL) algorithms are the central focus of modern machine learning systems. As data volumes keep growing, it has become customary to train large neural networks with hundreds of millions of parameters to maintain enough capacity to memorize these volumes and obtain state-of-the-art accuracy. To get around the costly computations associated with large models and data, the community is i
Instagram Server is entirely Python powered. Well, mostly. There’s also some Cython, and our dependencies include a fair amount of C++ code exposed to Python as C extensions. Our server app is a monolith, one big codebase of several million lines and a few thousand Django endpoints [1], all loaded up and served together. A few services have been split out of the monolith, but we don’t have any pla
January 22, 2020 When I first joined Netlify over 2 years ago, I took over sprint planning for a while. As many teams do, they were doing a sort of scrum-lite. They didn’t have an estimation scale and we all agreed at various times that the traditional scrum scales of t-shirt sizes or fun bucks didn’t really capture the concept of scoring complexity. So, I came up with a scale I thought captured c
TL;DR時間がないので大規模アジャイルのヘルスメトリクスだけ手っ取り早く知りたいという方のために、メトリクスは以下です。 ちょうど1スプリントよりも長く存在するバックログの量エンドユーザーが追加説明なしで理解できるプロダクトバックログアイテムの割合開発者1人あたりの1日のコミット数トランクへ直接コミットする割合スプリントで選択されたPBIのうち、前回のスプリントレビュー前には存在しなかったPBIの割合スプリント終了時の着手済みの未完了アイテムスプリントごとに進行中の祖先全チームがオフィスにいる週の日数完成の定義メトリクスを見て興味が湧いた方はぜひ続きを読んで見てください。 大規模アジャイルのヘルスメトリクスについて語る目的講演の中で、通常は特定の指標については話していない、メトリクスに関する組織のダイナミクスと、メトリクスが組織内でどのように使用されているかに興味があるからだと言っていま
Infrastructure Testing Twitter.com: achieving reliable test results at scale Love it or hate it, testing is a vital part of any software development cycle. Since it would be highly undesirable for buggy code to be deployed to the millions of devices accessing Twitter. Twitter.com has thousands of unit tests and nearly a hundred integration tests. Integration tests, also known as smoke tests, allow
GhostDB stemmed from a University project. Due to the nature of these projects (time constraints etc.), we feel some corners were cut. For example, we opted for the memcached model of distribution to save on time as it was easier to implement. However, this wasn't the original vision of GhostDB. Myself and Connor also started new jobs and these took up a good chunk of our time. This combined with
Jason Warner is the CTO of GitHub, the world’s leading open-source platform with 40M users. He is responsible for GitHub’s product, engineering, support, and security departments. Prior to GitHub, Jason was VP of Engineering at Heroku, one of the largest cloud computing providers in the world. And before that, Jason worked to make Linux accessible to everyone by leading product engineering for Ubu
Explore Azure Get to know Azure Discover secure, future-ready cloud solutions—on-premises, hybrid, multicloud, or at the edge Global infrastructure Learn about sustainable, trusted cloud infrastructure with more regions than any other provider Cloud economics Build your business case for the cloud with key financial and technical guidance from Azure Customer enablement Plan a clear path forward fo
こんばんは(?)、セキュリティエンジニアリングの西川です。 re:Invent始まりましたね。 カミナシからも今年は4名参加して、AWSの知識・技術向上のためさまざまなセッションに参加しています。 私は今回re:Inventは初参加で、右も左もわからない状態ですが、その中でも特に楽しみにしていたセッションがありまして、それが「COP402: Coding for compliance at scale」でした。 このセッションはレベルが400(Expert)という一番高いものでしたが、コードを見ながら解説がされていくため元々開発者だった私にとっては非常にわかりやすかったのと、 RDK(AWS Config Rule Development Kit)を試そうとして色々と調べていたので、英語はあまり得意ではないのですが理解することができました。 AWS Configのカスタムルールが書けるのは
Yes, the illustrations you find on scale are literally "royalty-free vector illustrations for commercial use" - absolutely free and no attribution needed. Academic, personal or commercial use, doesn't matter - feel free to use Scale's illustrations! The only condition is to not use our illustrations to resell, or repackage. Basically don't duplicate Scale or create a competitor product. Otherwise,
The metadata files are parquet files that contain the following attributes: URL, TEXT, the cosine similarity score between the text and image embedding and height and width of the image. Watermark and safety tags can be joined with the metadata prior to downloading by using this script. Once that is done, they can easily be filtered upon with a probability threshold at your choice (we recommend 0.
AWS News Blog Amazon Kinesis Data Streams On-Demand – Stream Data at Scale Without Managing Capacity Today we are launching Amazon Kinesis Data Streams On-demand, a new capacity mode. This capacity mode eliminates capacity provisioning and management for streaming workloads. Kinesis Data Streams is a fully-managed, serverless service for real-time processing of streamed data at a massive scale. Ki
AWS is launching additional APIs to create, read, update and delete users and groups in AWS IAM Identity Center (successor to AWS Single Sign-On). The new APIs expand existing capabilities to help reduce administrative effort and save time, and provide greater visibility into the users and groups that are available in IAM Identity Center. You can use the APIs for provisioning, de-provisioning or u
Vector Search Engine for the next generation of AI applications Qdrant (read: quadrant) is a vector similarity search engine and vector database. It provides a production-ready service with a convenient API to store, search, and manage points—vectors with an additional payload Qdrant is tailored to extended filtering support. It makes it useful for all sorts of neural-network or semantic-based mat
- Source: CNN " data-fave-thumbnails="{"big": { "uri": "https://media.cnn.com/api/v1/images/stellar/prod/230315020929-01-japan-population-village-cnn-031523.jpg?c=16x9&q=h_540,w_960,c_fill" }, "small": { "uri": "https://media.cnn.com/api/v1/images/stellar/prod/230315020929-01-japan-population-village-cnn-031523.jpg?c=16x9&q=h_540,w_960,c_fill" } }" data-vr-video="false" data-show-html="" data-byli
git-branchless is a suite of tools which enhances Git in several ways: It makes Git easier to use, both for novices and for power users. Examples: git undo: a general-purpose undo command. See the blog post git undo: We can do better. The smartlog: a convenient visualization tool. git restack: to repair broken commit graphs. Speculative merges: to avoid being caught off-guard by merge conflicts. I
Our nine month journey to horizontally shard Figma’s Postgres stack, and the key to unlocking (nearly) infinite scalability. Vertical partitioning was a relatively easy and very impactful scaling lever that bought us significant runway quickly. It was also a stepping stone on the path to horizontal sharding. Figma’s database stack has grown almost 100x since 2020. This is a good problem to have be
Ankerのスマートホームブランド・Eufy初となる体重体組成計「Eufy Smart Scale P2 Pro」が2022年8月30日に登場しました。単に体重を量るだけではなく、スマートフォンにアクセスして心拍数・体脂肪率・BMI・筋肉量・水分量・基礎代謝量・内臓脂肪量・体脂肪量・除脂肪体重・骨量(kg)・タンパク質・体内年齢・骨格筋量・皮下脂肪率・体脂肪率・ボディタイプの全16項目を測定でき、さらに自分の体型を3Dモデル化して示してくれるとのことで、Eufy Smart Scale P2 Proを実際にスマートフォンと連携させる設定を行ってみました。 Eufy Smart Scale P2 Pro | 体重・体組成計の製品情報 https://www.ankerjapan.com/collections/eufy-smart-scale/products/t9149 Eufy Smar
Welcome to our series of case studies about companies using Elixir in production. See all cases we have published so far. Founded in 2015 by Jason Citron and Stan Vishnevskiy, Discord is a permanent, invite-only space for your communities and friends, where people can hop between voice, video, and text, depending on how they want to talk, letting them have conversations in a very natural or authen
Ankerのスマートホームブランド・Eufyから、初の体重体組成計となる「Eufy Smart Scale P2 Pro」が2022年8月30日に登場しました。Wi-FiやBluetooth接続に対応しており、スマートフォンにアクセスし、体重だけではなく心拍数や体脂肪率、BMI、筋肉量、水分量、基礎代謝量、内臓脂肪量、体脂肪量、除脂肪体重、骨量(kg)、タンパク質、体内年齢、骨格筋量、皮下脂肪率、体脂肪率、ボディタイプの全16項目を測定可能。そんな先端技術が詰め込まれた体重計がどんなものかを体験するべく、まずはその見た目をチェックしてみました。 Eufy Smart Scale P2 Pro | 体重・体組成計の製品情報 https://www.ankerjapan.com/collections/eufy-smart-scale/products/t9149 Eufy Smart Sca
Scrum@Scaleガイドの序文 スクラムは、もともとスクラムガイドで説明されているように、単一のスクラムチームが、持続可能なペースを維持しつつ、最適な価値を提供できるようにすることを重視している。スクラムガイドの発行以降、スクラムの利用はプロダクト、プロセス、サービスなどの開発といった複数チームの協力が求められる領域まで広がりを見せている。 現場では、組織内のスクラムチーム数の増加に伴い、2つの重要な問題の発生が繰り返し見られた。 複数のチームの間での依存関係や作業の重複、コミュニケーションのオーバーヘッドなどの問題により、チームごとのアウトプット(動作するプロダクト)の量、スピード、品質が低下し始めた。 従来の組織構造はビジネスアジリティの実現に十分な効果を上げられなかった。優先順位の競合や、市場の変化に対応するようチームを素早く転換させることができないなどの問題が発生した。 こうし
Scrum@Scaleに挑戦するスペシャリストが語る、今の時代スクラムマスターに求められるもの 2022年2月1日 Chatwork株式会社 エンジニアリングマネージャー 粕谷 大輔 2001年に大学卒業後、SI、ソーシャルゲーム開発を経て、SaaSサービスの開発エンジニアやディレクターを経験。2021年よりChatwork株式会社にてScrum@Scaleをベースにした開発組織づくりに携わっている。共著に『Mackerelサーバ監視[実践]入門』(技術評論社)、『開発現場に伝えたい10のこと』(達人出版会)。アドバンスド認定スクラムマスター/認定スクラムプロダクトオーナー。 チームメンバーがスクラムの理論を理解し正しく実践できるよう、障害物を取り除いたり、問題解決を促したりする「スクラムマスター」。アジャイル開発の推進に伴い、近年そのニーズが高まっている。 Chatworkでは根幹部分
As a team, we’re thrilled to spend time with the developer community at Microsoft Build. Windows is a place where people come to create, to learn, and to connect. One of the most energizing aspects of Windows is how the developer community has been engaging with the platform, bringing value to over a billion people across the planet. Windows is the platform for the world’s innovation, and develope
Microsoft is dedicated to working with the community and our customers to continuously improve and tune our platform and products to help defend against the dynamic and sophisticated threat landscape. Earlier this year, we announced that we would replace the existing software testing experience known as Microsoft Security and Risk Detection with an automated, open-source tool as the industry moved
PostgreSQL Advent Calendar 2019の14日目です。 PG-Stromの開発をやってると、しばしば聞かれるのが 『マルチノードの並列処理って対応してるんですか?』 という質問。 まぁ、『対応しておりませんし、対応する予定もございません』という回答になるんですが、別にこれはウチのやる気の問題ではなく、PG-StromはPostgreSQLの拡張モジュールとして設計されているため、並列分散処理に関しては他のメカニズムに任せてしまえばよい、というだけの話である。 そこで、今回は同じくPostgreSQLの拡張モジュールとして実装されているスケールアウト機能の Citus と、PG-Stromを組み合わせてちゃんと動作するんですよという事を検証してみる事にする。 Citusとは? PostgreSQLにデータ分散と並列処理機構を付加する拡張モジュールで、PostgreSQ
Practical Approach to Automate the Discovery and Eradication of Open- Source Software Vulnerabilities at Scale Aladdin Almubayed Senior Application Security Engineer @ Netflix @0xshellrider @ @ Outline • The problem of open source security (5 minutes) • Attacks on open source dependencies (10 minutes) • Our approach (25 minutes) • Challenges & Future work (5 minutes) @ @0xshellrider Aladdin Almuba
Wonder Scale ------------------------------ EN Credits. ▶Music Song by China Kuramoto (VA. Mao Ito) Lyric written by Shoko Ohmori Composed, Arranged by Shu Kanematsu Strings : Koichiro Muroya Strings Concertmaster : Koichiro Muroya 1st Violin : Mariko Aikawa, Teruka Murata, Tetsuo Tsushima, Yuichi Endo, Eriko Ukimura 2nd Violin : Rina Odera, Lisa Yamamoto, Akane Irie, Mamiko Amemiya Viola : Miki
Serverless-artillery Introduction Combine serverless with artillery and you get serverless-artillery (a.k.a. slsart). Serverless-artillery makes it easy to test your services for performance and functionality quickly, easily and without having to maintain any servers or testing infrastructure. Use serverless-artillery if You want to know if your services (either internal or public) can handle diff
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