Cloud AI helps you train and serve TensorFlow TFX pipelines seamlessly and at scale | Google Cloud Blog
Cloud AI helps you train and serve TensorFlow TFX pipelines seamlessly and at scale Last week, at the TensorFlow Dev Summit, the TensorFlow team released new and updated components that integrate into the open source TFX Platform (TensorFlow eXtended). TFX components are a subset of the tools used inside Google to power hundreds of teams’ wide-ranging machine learning applications. They address cr
Meson: Workflow Orchestration for Netflix Recommendations
At Netflix, our goal is to predict what you want to watch before you watch it. To do this, we run a large number of machine learning (ML) workflows every day. In order to support the creation of these workflows and make efficient use of resources, we created Meson. Meson is a general purpose workflow orchestration and scheduling framework that we built to manage ML pipelines that execute workloads
Orchestra
OrchestraAn open-source human-in-the-loop AI system to orchestrate teams of experts and machines Humans where they're best, machines for the restOrchestra automates repetitive, rules-based project tasks so team members can focus on creative and analytical work, learn new skills, and mentor others. Using a human-in-the-loop AI system like Orchestra will improve your project management processes, re
Engineering
Engineering Building the platform where people around the world come to search, save and shop Our Engineering team is at the core of bringing our platform to life for Pinners worldwide. Working collaboratively and cross-functionally with teams across the company, our engineers tackle growth-driving challenges to build an inspired and inclusive platform for all. Natasha Magliui Engineering Manager,
Cloudera: 機械学習が広がる中でのデータモデル - ワザノバ | wazanova
Josh Willsは、Googleで広告オークションシステムとデータ分析インフラの開発を担当し、現在は、ClouderaのSenior Director of Data Scienceを勤めています。 機械学習によるデータ分析が、Googleなどに限らず、AirbnbやEtsyなどその他のネット企業にも広がっていく中、今後必要とされるデータモデルについて 、Midwest.io と GraphLab Conference で講演しています。 複数の機械学習分析を操るGoogleのチャレンジ 機械学習分析のベースとなる各サービスの仕様が頻繁に変更になる。 機械学習分析はそれぞれ単体でも複雑なのに、Googleの場合、それらが相互に関連があるケースが多かった。一つの機械学習分析の変更が、他の機械学習にどう影響がでるかが把握しきれない。その結果、広告配信システムを理解できない（= コントロール
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