機械学習を「社会実装」するということ 2023年版 / Social Implementation of Machine Learning 2023機械学習を「社会実装」する際に待ち受けている罠と、その解決方法の考察 (2023年版) です。今回は、機械学習プロジェクトに取り組む私たちに何ができるか？といった内容を盛り込みました。 ※この資料は、東京大学メタバース工学部リスキリング工学教育プログラム GCI 2022 Winterの講義で使用…
The Death of Central ML Is Greatly Exaggerated
Over the last decade, the industry has gone from celebrating the rise of the “central ML team” to questioning whether it should exist. I can’t help but feel like I’m watching Rome burn. It doesn’t have to be this way. Why It’s Becoming Trendy To Bash Central MLAs the emerging field of machine learning operations (MLOps) continues to grow rapidly and new tools and techniques proliferate, the potent
1. 単回帰 — 機械学習帳
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Companion webpage to the book "Mathematics for Machine Learning"
Companion webpage to the book "Mathematics for Machine Learning". Copyright 2020 by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong. Published by Cambridge University Press. View the Project on GitHub View On GitHub Please link to this site using https://mml-book.com. Twitter: @mpd37, @AnalogAldo, @ChengSoonOng. We wrote a book on Mathematics for Machine Learning that motivates people to
Mehryar Mohri -- Foundations of Machine Learning - Book
Download (official online versions from MIT Press): book (PDF, HTML). lecture slides. Hardcopy (MIT Press, Amazon). Errata (printing 1). Foundations of Machine Learning Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar MIT Press, Second Edition, 2018. Copyright in this Work has been licensed exclusively to The MIT Press, http://mitpress.mit.edu, under a Creative Commons CC-BY-NC-ND license.
Challenges for Machine Learning Systems toward Continuous Improvement
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The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design
The past decade has seen a remarkable series of advances in machine learning, and in particular deep learning approaches based on artificial neural networks, to improve our abilities to build more accurate systems across a broad range of areas, including computer vision, speech recognition, language translation, and natural language understanding tasks. This paper is a companion paper to a keynote
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
KDD 2019 | 150 successful Machine Learning models: 6 lessons learned at Booking.com
150 successful Machine Learning models: 6 lessons learned at Booking.com Booking.com is the world’s largest online travel agent where millions of guests find their accommodation and millions of accommodation providers list their properties including hotels, apartments, bed and breakfasts, guest houses, and more. During the last years we have applied Machine Learning to improve the experience of ou
Accelerating the Machine Learning Lifecycle with MLflow 1.0 | M. Zaharia, A. Davidson, G. Buehrer
Last year, Databricks launched MLflow, an open source framework to manage the machine learning lifecycle that works with any ML library to simplify ML engineering. MLflow provides tools for experiment tracking, reproducible runs and model management that make machine learning applications easier to develop and deploy. In the past year, the MLflow community has grown quickly: 80 contributors from o
Google Cloud Next ’19 in Tokyo にて講演したセッション動画が公開されました
2019.10.15 Google Cloud Next ’19 in Tokyo にて講演したセッション動画が公開されました #登壇・資料公開 #MLOps 2019年7月30日～8月1日に開催されたGoogle Cloud Next ’19 in Tokyoにてデータサイエンティストの田中一樹とMLエンジニアの春日瑛が登壇しました。 その際のセッション動画が公開されましたので、ぜひご覧ください。 『逆転オセロニア』の AI 機能を支える GCP セッション詳細 アプリゲーム『逆転オセロニア』において提供中のAI機能と、その裏側のスケーラブルかつロバストなシステムをGCP上で構築するノウハウについて紹介しています。プレイヤーに合わせて適切なデッキをレコメンドするAIと人間のように対戦できるAIを取り上げ、GCP上での具体的な実現方法やAIの技術的な詳細、リリース後のシステムパフォーマンス
Survey and Benchmarking of Machine Learning Accelerators
Advances in multicore processors and accelerators have opened the flood gates to greater exploration and application of machine learning techniques to a variety of applications. These advances, along with breakdowns of several trends including Moore's Law, have prompted an explosion of processors and accelerators that promise even greater computational and machine learning capabilities. These proc
Ask HN: What does your ML pipeline look like? | Hacker News
Experienced machine learning professionals - How do you create scalable, deployable and reproducible data/ML pipelines at your work? As a DevOps Engineer working for a ML-based company and have had worked for others in the past, these are my quick suggestions for production readiness.DOs: If you are doing any kind of soft-realtime (i.e. not batch processing) inference, by exposing a model on a req
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GitHub - replicate/cog: Containers for machine learning
Michelangelo - Machine Learning @Uber
GitHub - chris-chris/ml-engineer-roadmap: WIP: Roadmap to becoming a machine learning engineer in 2020
Mathematics for Machine Learning - Linear Algebra
Ask Me Anything session with a Kaggle Grandmaster Vladimir I. Iglovikov
MLPerf Training Benchmark
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