Realtime Data Processing at Facebook - Meta Research | Meta Research
Realtime data processing powers many use cases at Facebook, including realtime reporting of the aggregated, anonymized voice of Facebook users, analytics for mobile applications, and insights for Facebook page administrators. Many companies have developed their own systems; we have a realtime data processing ecosystem at Facebook that handles hundreds of Gigabytes per second across hundreds of dat
TFX: A TensorFlow-Based Production-Scale Machine Learning Platform
Author: Heng-ÂTze Cheng, Google Research New York, Google, Inc. Abstract: Creating and maintaining a platform for reliably producing and deploying machine learning models requires careful orchestration of many components—-a learner for generating models based on training data, modules for analyzing and validating both data as well as models, and finally infrastructure for serving models in produ
Rules of Machine Learning: Â |Â Google for Developers
Rules of Machine Learning: Stay organized with collections Save and categorize content based on your preferences. Best Practices for ML Engineering Martin Zinkevich This document is intended to help those with a basic knowledge of machine learning get the benefit of Google's best practices in machine learning. It presents a style for machine learning, similar to the Google C++ Style Guide and othe
HoloClean - Home
A Machine Learning System for Data Repair and Predictions on Structured Data HoloClean is a statistical inference engine to impute, clean, and enrich data. As a weakly supervised machine learning system, HoloClean leverages available quality rules, value correlations, reference data, and multiple other signals to build a probabilistic model that accurately captures the data generation process, and
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BigQueryã§ç‰¹å®šã®ãƒ‡ãƒ¼ã‚¿ã‚»ãƒƒãƒˆã ã‘共有ã™ã‚‹æ–¹æ³• - Qiita
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What Do Data Scientists Need to Know about Containerization? As Little as Possible. | Stitch Fix Technology – Multithreaded
https://db-event.jpn.org/deim2017/papers/108.pdf
https://db-event.jpn.org/deim2018/data/papers/265.pdf
ML Solutions Lab
SysML 18: Jeff Dean, Systems and Machine Learning Symbiosis
http://www.sysml.cc/
https://arxiv.org/pdf/1702.00820.pdf
GitHub - HoloClean/holoclean: A Machine Learning System for Data Enrichment.
http://learningsys.org/nips17/assets/papers/paper_13.pdf
Introducing MLflow: an Open Source Machine Learning Platform
Data Management in Machine Learning: Challenges, Techniques, and Systems
http://cseweb.ucsd.edu/~arunkk/
http://db.ucsd.edu/
DEEM: Workshop on Data Management for End-to-End Machine Learning @ SIGMOD'18
Microsoft Word - infovis05.doc
The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations - Visual Languages, 1996. Proceedings., IEEE Symposium on
The Art and Science of Leadership at Scale: 3 Lessons from Sheryl Sandberg
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