Andrew Zhai | Pinterest tech lead, Visual Search Recently, we announced Lens BETA, a new way to discover objects and ideas from the world around on you using your phone’s camera. Just tap the Lens icon in the Pinterest app, point it at anything and Lens will return visually similar objects, related ideas or the object in completed projects or contexts. Lens enables you to go beyond traditional use
Dear Friends, I will be resigning from Baidu, where I have been leading the company’s AI Group. Baidu’s AI is incredibly strong, and the team is stacked up and down with talent; I am confident AI at Baidu will continue to flourish. After Baidu, I am excited to continue working toward the AI transformation of our society and the use of AI to make life better for everyone. Artificial Intelligence at
Today we are incredibly excited to announce the open source release of StreamAlert, a real-time data analysis framework with point-in-time alerting. StreamAlert is unique in that it’s serverless, scalable to TB’s/hour, infrastructure deployment is automated and it’s secure by default. In this blog post, we’ll cover why we built it, additional benefits, supported use-cases, how it works and more! W
I joined Facebook in 2011 as a business intelligence engineer. By the time I left in 2013, I was a data engineer. I wasn’t promoted or assigned to this new role. Instead, Facebook came to realize that the work we were doing transcended classic business intelligence. The role we’d created for ourselves was a new discipline entirely. My team was at forefront of this transformation. We were developin
You know Big Data… it’s a dirty business. All the literature shows you how powerful all those data crunchers and query engines are, but it all assumes that all the data is ready to be consumed. In reality a lot of automated Dataflow, Spark and BigQuery ETL processes are glued together with bash or Python. Well it’s time to change that… and to take a look at Apache Airflow. Airflow is a workflow en
Distilling a generally-accepted definition of what qualifies as artificial intelligence (AI) has become a revived topic of debate in recent times. Some have rebranded AI as “cognitive computing” or “machine intelligence”, while others incorrectly interchange AI with “machine learning”. This is in part because AI is not one technology. It is in fact a broad field constituted of many disciplines, ra
S3 is an object store and not a file system, hence the issues arising out of eventual consistency, non-atomic renames have to be handled in the application code. The directory server in a filesystem has been replaced by a hash algorithm of the filename. This is bad for listing things, directory operations, deleting and renaming(copying and deleting as technically there is no renaming in object sto
人には様々な事情があり、先の「海外移住?アメリカは止めた方がいいよ」の記事にも追記で書いてあるけれど、日本に比べたらたしかにアメリカ(というかシリコンバレー)は税金やら医療費やら諸々でかかるお金は多いと思うし、実質的な生活水準(職場以外)を考えると負担は大きいと思う。 一方で類さんが言うように、エンジニア職(プログラマー、ソフトウェアエンジニア、インフラエンジニア等々)にとってみたら天国みたいな会社もたくさんあるし、そこしかないチャレンジというのは数多くある。特にソフトウェアプロダクトはシリコンバレー発の物が多いし、その周辺企業同士のコミュニティも存在するので、そういった世界の真っ只中に飛び込みたい人には素晴らしい環境だろう。 所得以外に得られる経験に金額はつけられないと思うけれど、同じ生活水準で過ごすことを考えたらチャレンジできるのであれば、そこに行く人は応援したいと思うし、そういう人が
Medium開き。はてなで自分用のブログを持っているけど、urlとかidとかがおかしいし、直したいのに直せないので、こっちに移ることにした。 僕がやるべきではないと思った仕事まず断りを入れておくと、仕事と言っても、職場の話ではない。 GoとかReduxとかReactとか、NodeとかRailsとか、OSSで見ておかなきゃなーと思った公式リポジトリをフォローした時の話。 僕は割とOSSにバンバンコミットするぜ、みたいなことに憧れつつも、なかなか自分だけのソフトウェアを作ってしまう性格で、それを治したいと思っていた。 とりあえず手始めに、各リポジトリをフォローして、各言語やソフトウェアがどんな課題を感じているのか、調べることにしようと思った。 その時に問題が発生して、なんとスパムのようにNotifyメールが来る。どれくらい凄いかというと、特にReduxなんだけど、日本時間で深夜3時くらいに、3
Airflow, the workflow scheduler we use, recently hit version 1.6.1, and introduced a revamp of its scheduling engine. We like it because the code is easy to read, easy to fix, and the maintainer, Maxime Beauchemin, is very responsive. We also like that it’s all code, rather than using config files like xml to describe the dags. Nevertheless, there are a few things that are less than obvious that w
Airbnb’s data infrastructure has been an essential part of our strategy to continuously improve our products. Our Hive data warehouse grew exponentially from 350TB in the middle of 2013 to 11PB by the end of 2015. As the company grew, the demands on the reliability of the warehouse grew as well, and we sought to migrate our warehouse to new architectures. We found that existing migration tools eit
ProblemA responsibility for the Data team at Airbnb is to scale the ability to make decisions using data. We democratize data access to empower all employees to make data-informed decisions, give everybody the ability to use experiments to correctly measure the impact of their decisions, and turn insights on user preferences into data products that improve the experience of using Airbnb. Recently,
Data scientists at Airbnb collect and use data to optimize products, identify problem areas, and inform business decisions. For most guests, however, the defining moments of the “Airbnb experience” happen in the real world — when they are traveling to their listing, being greeted by their host, settling into the listing, and exploring the destination. These are the moments that make or break the A
One of my favorite things about being a data scientist at Airbnb is collaborating with a diverse team to solve important real-world problems. We are diverse not only in terms of gender, but also in educational backgrounds and work experiences. Our team includes graduates from Mathematics and Statistics programs, PhDs in fields from Education to Computational Genomics, veterans of the tech and fina
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