Real-time Recommendations using Spark Comcast LabsDatabricks is the Data and AI company. More than 10,000 organizations worldwide — including Block, Comcast, Conde Nast, Rivian, and Shell, and over 60% of th...
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Michelangelo PyML: Introducing Uber's Platform for Rapid Python ML Model Development
AIMichelangelo PyML: Introducing Uber’s Platform for Rapid Python ML Model DevelopmentOctober 23, 2018 / Global As a company heavily invested in AI, Uber aims to leverage machine learning (ML) in product development and the day-to-day management of our business. In pursuit of this goal, our data scientists spend considerable amounts of time prototyping and validating powerful new types of ML model
Systems for ML
Workshop on Systems for ML and Open Source Software at NeurIPS 2018 Workshop on Systems for ML A new area is emerging at the intersection of artificial intelligence, machine learning, and systems design. This birth is driven by the explosive growth of diverse applications of ML in production, the continued growth in data volume, and the complexity of large-scale learning systems. The goal of this
OpML '19
The 2019 USENIX Conference on Operational Machine Learning (OpML '19) provides a forum for both researchers and industry practitioners to develop and bring impactful research advances and cutting edge solutions to the pervasive challenges of ML production lifecycle management. ML production lifecycle is a necessity for wide-scale adoption and deployment of machine learning and deep learning across
Jupyter だけで機械学習を実サービス展開できる基盤 - Tech Blog - Recruit Lifestyle Engineer
こんにちは、CET チームの田村です。データ基盤を構築・運用したり、チャットボット（スマホ用です）を開発したりしているエンジニアです。 皆さん、実サービスで機械学習、活用できていますか？ 正直、難しいですよね。高精度なモデルを作ること自体も難しいですが、実際のサービスにそれを組み込むには、そこからさらに数々の難所が待ち構えているからです。 でも、そのほとんどはエンジニアリングで解消できます。 私たちのチームでは、数年にわたる経験をもとに難所とその対処法を整理し、すばやく成果をあげられる機械学習基盤を開発しはじめました。 本記事では、この基盤の設計とその背後にあるアイデアをご紹介します（機械学習工学研究会の勉強会での発表資料がベースです）。 イテレーションを何度も回せ 基盤そのものの前に、まず機械学習を成果につなげるためのポイントを説明させてください。 私たちは、機械学習の活用において必要な
Machine Learning Schema Community Group
This group represents a collaborative, community effort with a mission to develop, maintain, and promote standard schemas for data mining and machine learning algorithms, datasets, and experiments. Our target is a community agreed schema as a basis for ontology development projects, markup languages and data exchange standards; and an extension model for the schema in the area of data mining and m
ML Systems
Workshop on ML Systems at NIPS 2017 December 8, 2017 Home Call for Papers Schedule Speakers Accepted Papers ML Systems Workshop A new area is emerging at the intersection of artificial intelligence, machine learning, and systems design. This birth is driven by the explosive growth of diverse applications of ML in production, the continued growth in data volume, and the complexity of large-scale le
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http://learningsys.org/nips18/assets/papers/15CameraReadySubmissionneurips_sysml_2018-8.pdf
http://www.vldb.org/pvldb/vol11/p1781-schelter.pdf
full-issue
http://sites.computer.org/debull/A18dec/p5.pdf
マイクロチームでの機械学習PoC / ShibuyaSynapse #4,
Data Management Challenges in Production Machine Learning
http://learningsys.org/nips17/assets/papers/paper_32.pdf
TensorFlow Extended (TFX) (TensorFlow Dev Summit 2018)
http://learningsys.org/nips17/assets/papers/paper_1.pdf
What Do Data Scientists Need to Know about Containerization? As Little as Possible. | Stitch Fix Technology – Multithreaded
SysML 18: Jeff Dean, Systems and Machine Learning Symbiosis
http://www.sysml.cc/