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Aki Ariga

Machine Learning Engineer

Arm Treasure Data

Biography

Aki Ariga is a Machine Learning Engineer at Arm Treasure Data. His interests include developing peroduction Machien Learning systems, Machine Learning products, and ML Ops. He aims to leverage Machine Learning powers and technologies for business and social good.

He lead several communities at Tokyo such as Machine Learning Casual Talks, kawasaki.rb, and he is also one of the organizers of “Working Group of Machine Learning systems and operations for productionization” in Special Interest Group on Machine Learning System Engineering.

Interests

  • Machine Learning
  • ML Ops
  • Natural Language Processing

Education

  • MEng in Electrical Engineering and Computer Science, 2008

    Nagoya University

  • BSc in Electrical Engineering and Computer Science, 2006

    Nagoyua University

Recent Posts

How to release Python package from GitHub Actions

Photo by Hitesh Choudhary on Unsplash Recently, I changed my CI from Travis to GitHub Actions. GitHub Actions is handy and useful for …
2019-11-26 Python

How to test a new Docker image for digdag workflow on CircleCI?

Photo by Campaign Creators on Unsplash Testing workflow runnability would be important when we build a complex workflow. digdag is a …
2019-10-06

The first conference of Operational Machine Learning: OpML ‘19

I attended OpML ’19 is a conference for “Operational Machine Learning” held at Santa Clara on May 20th. OpML ‘19 _The 2019 USENIX …
2019-06-04 mlops, machine_learning

Ruby for Data Science and Machine Learning

I attended RubyKaigi 2019 held at Fukuoka from Apr 18 to Apr 21. This year’s RubyKaigi was a really great opportunity for me to know …
2019-04-24 machine_learning, ruby

A recent update of tabula-py

Photo by Joshua Rawson-Harris on Unsplash This article is a repost of Patreon article published last December. I’m planning to bump up …
2019-02-18 OSS
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Recent Posts (in Japanese)

Pythonistaのためのdigdag py> operator開発ガイド

Table of Contents workflowをpushするまえにローカル環境で開発とテストをする方法 基本戦略:Pythonのタスクを合理的な粒度にまとめる 1. Treasure Da
2019-12-24 10 min read Python, TreasureData

IBIS 2019の機械学習工学企画セッションに登壇しました

ML in productionの課題について話しました
2019-12-05 4 min read Machine Learning, MLOps, conference

RとTreasure Data

RTDとRPrstoを使ってRからTreasure Dataにアクセスしてみよう
2019-12-04 3 min read R, TreasureData

『n月刊ラムダノート』創刊記念パーティに登壇しました

さる5/30に『n月刊ラムダノート』創刊記念パーティーという名のコルーチン祭りを開催しました。 会の中で一番嬉しそうだった鹿野さん 同僚の Toru Takahashi さ
2019-06-04 2 min read

「n月刊ラムダノート」の記事の書き方～「MLOpsの歩き方」編

先日、ラムダノートから創刊された「n月刊ラムダノート」に「MLOpsの歩き方」を寄稿しました。 『n月刊ラムダノート』創刊のお知らせ _このたび
2019-03-29 8 min read book, writing
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OSS / notebooks

tabula-py

Extract you tables in PDF into pandas DataFrame

Machine Learning in Production Wiki

Machine Learning infrastructure/architecture/operation for productionization

Docker Sphinx Recommonmark

Sphinx documentation toolchain, including latex and recommonmark in an Ubuntu docker container

Notebooks

tutorial machine learning or data science, written in Japanese

cookiecutter-digdag

A template generates digdag workflows for SQL and Python

tdworkflow

Unofficial Treasure Workflow Client

RTD

Simple R client for Treasure Data

Mykytea

Python/Ruby wrapper for KyTea

Recent & Upcoming Talks

Challenges for Machine Learning Systems toward Continuous Improvement

When executing machine learning pipelines for trainings and inferences, the systems and machine learning infrastructures vary depending …
2019-11-22 14:20 — 15:00 Winc Aichi, Nagoya
Slides

How do you debug/test your Workflow?

Developping and testing for workflows productively is hard. In this session, I talk about how to develop heavy data dependent workflow …
2019-08-30 — Shinjuku, Tokyo
Slides

Managing Machine Learning workflows on Treasure Data

2018-10-17 — Shibuya, Japan
Slides

仕事ではじめる機械学習

2018-05-17 — Tokyo, Japan
Slides

Train, predict, and serve: How to put your machine learning model into production

Adopting a machine learning system is an essential step for enterprise companies to progress to the next stage of their business. …
2017-12-06 13:45 — 14:25 Suntec Singapore Convention & Exhibition Centre, Singapore
Slides
See all talks

Recent Publications

Quickly discover relevant content by filtering publications.

MLOpsの歩き方 (Beginners Guide to MLOps)

This article covers very biginning guide for MLOps, i.e., What is MLOps? How do tech giants make Machine Learning systems? What …
Paper book E-book

仕事ではじめる機械学習 (Machine Learning for Business)

First book for how to design Machine Learning systems and how to proceed Machine Learning projects. This book is originally written in …
Code E-book Paper book