機械å¦ç¿’を「社会実装ã€ã™ã‚‹ã¨ã„ã†ã“㨠2023年版 / Social Implementation of Machine Learning 2023
機械å¦ç¿’を「社会実装ã€ã™ã‚‹éš›ã«å¾…ã¡å—ã‘ã¦ã„ã‚‹ç½ ã¨ã€ãã®è§£æ±ºæ–¹æ³•ã®è€ƒå¯Ÿ (2023年版) ã§ã™ã€‚今回ã¯ã€æ©Ÿæ¢°å¦ç¿’プãƒã‚¸ã‚§ã‚¯ãƒˆã«å–り組むç§ãŸã¡ã«ä½•ãŒã§ãã‚‹ã‹ï¼Ÿã¨ã„ã£ãŸå†…容を盛り込ã¿ã¾ã—ãŸã€‚ ※ã“ã®è³‡æ–™ã¯ã€æ±äº¬å¤§å¦ãƒ¡ã‚¿ãƒãƒ¼ã‚¹å·¥å¦éƒ¨ãƒªã‚¹ã‚リング工å¦æ•™è‚²ãƒ—ãƒã‚°ãƒ©ãƒ GCI 2022 Winterã®è¬›ç¾©ã§ä½¿ç”¨â€¦
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. å˜å›žå¸° — 機械å¦ç¿’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
Michelangelo - Machine Learning @Uber
Transcript I'm going to talk today about Machine Learning at Uber. There are three phases of the talk. The first one is to go over some of the interesting use cases of ML at Uber. Second piece is around looking at the first version of the platform that we built to support those use cases and many more. And then, the final section is more tailored to this track, which is around developer experience
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.
ML Ops: Machine Learning as an Engineering Discipline
So, your company decided to invest in machine learning. You have a talented team of Data Scientists churning out models to solve important problems that were out of reach just a few years ago. All performance metrics are looking great, the demos cause jaws to drop and executives to ask how soon you can have a model in production. It should be pretty quick, you think. After all, you already solved
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
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2019.10.15 Google Cloud Next ’19 in Tokyo ã«ã¦è¬›æ¼”ã—ãŸã‚»ãƒƒã‚·ãƒ§ãƒ³å‹•ç”»ãŒå…¬é–‹ã•ã‚Œã¾ã—㟠#登壇・資料公開 #MLOps 2019å¹´7月30日～8月1æ—¥ã«é–‹å‚¬ã•ã‚ŒãŸGoogle Cloud Next ’19 in Tokyoã«ã¦ãƒ‡ãƒ¼ã‚¿ã‚µã‚¤ã‚¨ãƒ³ãƒ†ã‚£ã‚¹ãƒˆã®ç”°ä¸ä¸€æ¨¹ã¨MLエンジニアã®æ˜¥æ—¥ç‘›ãŒç™»å£‡ã—ã¾ã—ãŸã€‚ ãã®éš›ã®ã‚»ãƒƒã‚·ãƒ§ãƒ³å‹•ç”»ãŒå…¬é–‹ã•ã‚Œã¾ã—ãŸã®ã§ã€ãœã²ã”覧ãã ã•ã„。 『逆転オセãƒãƒ‹ã‚¢ã€ã® AI 機能を支ãˆã‚‹ GCP セッション詳細 アプリゲーム『逆転オセãƒãƒ‹ã‚¢ã€ã«ãŠã„ã¦æä¾›ä¸ã®AI機能ã¨ã€ãã®è£å´ã®ã‚¹ã‚±ãƒ¼ãƒ©ãƒ–ルã‹ã¤ãƒãƒã‚¹ãƒˆãªã‚·ã‚¹ãƒ†ãƒ ã‚’GCP上ã§æ§‹ç¯‰ã™ã‚‹ãƒŽã‚¦ãƒã‚¦ã«ã¤ã„ã¦ç´¹ä»‹ã—ã¦ã„ã¾ã™ã€‚プレイヤーã«åˆã‚ã›ã¦é©åˆ‡ãªãƒ‡ãƒƒã‚をレコメンドã™ã‚‹AIã¨äººé–“ã®ã‚ˆã†ã«å¯¾æˆ¦ã§ãã‚‹AIã‚’å–り上ã’ã€GCP上ã§ã®å…·ä½“çš„ãªå®Ÿç¾æ–¹æ³•ã‚„AIã®æŠ€è¡“çš„ãªè©³ç´°ã€ãƒªãƒªãƒ¼ã‚¹å¾Œã®ã‚·ã‚¹ãƒ†ãƒ 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
GitHub - chris-chris/ml-engineer-roadmap: WIP: Roadmap to becoming a machine learning engineer in 2020
Mathematics for Machine Learning - Linear Algebra
MLPerf Training Benchmark
http://www.sysml.cc/
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