Monitoring Machine Learning Models in Production
Introduction Once you have deployed your machine learning model to production it rapidly becomes apparent that the work is not over. In many ways the journey is just beginning. How do you know if your models are behaving as you expect them to? What about next week/month/year when the customer (or fraudster) behavior changes and your training data is stale? These are complex challenges, compounded
AI Project Management Anti Pattern
Responsive Adventures: Dirty Tricks From The Dark Corners of Front-End
Continuous Delivery for Machine Learning
Automating the end-to-end lifecycle of Machine Learning applications Machine Learning applications are becoming popular in our industry, however the process for developing, deploying, and continuously improving them is more complex compared to more traditional software, such as a web service or a mobile application. They are subject to change in three axis: the code itself, the model, and the data
Facebook News Feed インフラ責任者がスマニュー新VPoEに。「社内開発プラットフォームを作り、エンジニアを全力支援する」 - スマQ（スマキュー）｜スマートニュースの「はたらく」をみつける
Youlin Li（左）と前田俊太郎（右） Facebook で News Feedのインフラ責任者を務めた Youlin Liが、スマートニュースの Vice President of Engineering, Backend System and Foundation に就任しました。 about.smartnews.com Youlin の来日に合わせて、Vice President of Ad Product の前田俊太郎がインタビューしました。Facebook でどんなことをしてきたのか、その経験を生かしてスマートニュースで何を成し遂げようと考えているのか、徹底的に聞きました（構成：スマートニュース通訳・翻訳チーム＆スマQ編集部）。 Facebook を支える「社内開発プラットフォーム」の重要性 前田 よろしくお願いします。まずは自己紹介を。 Youlin Youlin Li で
Python at Netflix
By Pythonistas at Netflix, coordinated by Amjith Ramanujam and edited by Ellen Livengood As many of us prepare to go to PyCon, we wanted to share a sampling of how Python is used at Netflix. We use Python through the full content lifecycle, from deciding which content to fund all the way to operating the CDN that serves the final video to 148 million members. We use and contribute to many open-sou
Making Netflix Machine Learning Algorithms Reliable
This document discusses making Netflix machine learning algorithms reliable. It describes how Netflix uses machine learning for tasks like personalized ranking and recommendation. The goals are to maximize member satisfaction and retention. The models and algorithms used include regression, matrix factorization, neural networks, and bandits. The key aspects of making the models reliable discussed
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