Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
What understood about that we've used LUIS thro...
Search
Sponsored
·
Your Podcast. Everywhere. Effortlessly.
Share. Educate. Inspire. Entertain. You do you. We'll handle the rest.
→
NAVITIME JAPAN
PRO
January 16, 2018
Technology
30
0
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
What understood about that we've used LUIS through the year
What understood that we've used LUIS through the year.
NAVITIME JAPAN
PRO
January 16, 2018
More Decks by NAVITIME JAPAN
See All by NAVITIME JAPAN
つよつよリーダーが 抜けたらどうする? 〜ナビタイムのAgile⽀援組織の変遷〜
navitimejapan
PRO
23
16k
実践ジオフェンス 効率的に開発するために
navitimejapan
PRO
3
1.1k
安全で使いやすいCarPlayアプリの 魅せ方:HIGと実例から学ぶ
navitimejapan
PRO
1
290
見えないユーザの声はログに埋もれている! ~ログから具体的なユーザの体験を数値化した事例紹介~
navitimejapan
PRO
6
3.4k
ユーザーのためなら 『デザイン』 以外にも手を伸ばせる
navitimejapan
PRO
2
1.9k
フツーのIT女子が、 Engineering Managerになるまで
navitimejapan
PRO
3
440
不確実性に打ち勝つOKR戦略/How to manage uncertainty with OKR strategy
navitimejapan
PRO
4
4k
アジャイルを小さいままで 組織に広める 二周目 / Agile Transformation in NAVITIME JAPAN iteration 2
navitimejapan
PRO
4
1.5k
変更障害率0%よりも「継続的な学習と実験」を価値とする 〜障害を「起こってはならないもの」としていた組織がDirtの実施に至るまで〜 / DevOps Transformation in NAVITIME JAPAN
navitimejapan
PRO
8
6.1k
Other Decks in Technology
See All in Technology
RSA暗号を手計算したくなること、ありますよね?? (20260615_orestudy6_rsa)
thousanda
0
240
脆弱性対応、どこで線を引くか
rymiyamoto
0
370
Microsoft Build Keynoteふりかえり
tomokusaba
0
120
Android の公式 Skill / Android skills
yanzm
0
130
手塩にかけりゃいいってもんじゃない
ming_ayami
0
390
2026TECHFRESH畢業分享會 - 原生還是跨平台? App 開發踩坑實錄
line_developers_tw
PRO
0
820
自律型AIエージェントは何を破壊するのか
kojira
0
150
プロダクト開発から業務改善コンサルまで。事業全体へ「染み出す」ことで広がるエンジニアの可能性
ham0215
0
100
Claude Code×Terraform IaC テンプレート駆動開発
itouhi
1
490
AI駆動開発を通して感じた、 AI時代のデザイナーの役割変化
whisaiyo
0
250
AGENTS.mdとSkillsで始めるAIエージェント活用
sonoda_mj
3
200
EventBridge Connection
_kensh
5
690
Featured
See All Featured
BBQ
matthewcrist
89
10k
The Cult of Friendly URLs
andyhume
79
6.9k
The Straight Up "How To Draw Better" Workshop
denniskardys
239
140k
Digital Projects Gone Horribly Wrong (And the UX Pros Who Still Save the Day) - Dean Schuster
uxyall
0
1.7k
Exploring the Power of Turbo Streams & Action Cable | RailsConf2023
kevinliebholz
37
6.5k
Build your cross-platform service in a week with App Engine
jlugia
234
18k
Designing for Timeless Needs
cassininazir
1
250
Technical Leadership for Architectural Decision Making
baasie
3
400
DevOps and Value Stream Thinking: Enabling flow, efficiency and business value
helenjbeal
1
230
Optimising Largest Contentful Paint
csswizardry
37
3.7k
Believing is Seeing
oripsolob
1
140
エンジニアに許された特別な時間の終わり
watany
107
250k
Transcript
What understood about that we’ve used LUIS through the year
Shinichi Tanabe January 12, 2018 Minami Aoyama Night #5
Speaker Shinichi Tanabe (田邊 晋一/たなべ しんいち) • NAVITIME JAPAN
Co., Ltd. ◦ Joined in 2008 ◦ Cogbot project ◦ Programmer
Products
None
Encounter
September 15, 2016
None
None
First impression
Easy to use, runs fast and smart.
Easy to use
Let’s go to the portal site! https://www.luis.ai
Step1. Create new app
None
Step2. Add intent
None
Step3. Add utterances
None
Step4. Add entities
None
None
Step5. Train
None
Step6. Test
None
None
None
None
None
Step7. Publish
None
None
That’s all!
Furthermore...
You can get a happy bonus.
Versioning
None
None
None
None
Runs fast and smart
Comparison between and LUIS Dialogflow
Test model
Test model Intent Places.FindPlace Utterances おいしいカレーが食べたいな どこか近くでおすすめのレストランを教えて Entities Cuisine カレー
PlaceType レストラン
Training speed
LUIS 2 - 4 sec Dialogflow 4 - 8 sec
The training speed result of test model
Precision and recall
Test utterance LUIS Dialogflow Intent Entity Intent Entity おいしいカレーが食べたいな 〇
〇 〇 〇 どこか近くでおすすめのレストラ ンを教えて 〇 〇 〇 〇 Precision result of test model
Test utterance LUIS Dialogflow Intent Entity Intent Entity おすすめのバーを教えて 〇
〇 × × おすすめのバー教えて 〇 × × × おいしいうどんが食べたい 〇 〇 〇 × おいしいうどん食べたい 〇 × 〇 × Recall result of test model
Yes, he was perfect!
Getting started
But, we had some questions.
Questions 1. How should we defines intents and entities? 2.
How do we know accuracy and precision? 3. When will he go GA(General Availability)?
1. How should we defines intents and entities?
Anti pattern Utterance : Intent ≒ 1 : 1
Use or copy pre-build model positively.
None
None
2. How do we know accuracy and precision?
Comprehensive test on model
Batch testing
Test result details in a visualized view.
Error matrix
True positive True negative Green zone indicates correct prediction
False negative False positive Red zone indicates incorrect prediction
3. When will he go GA?
LUIS is now GA!!
How to get along with LUIS
Points 1. Start small model which has few intents. 2.
Use or copy pre-build model positively. 3. Raise requests before do something about that yourself.
Thank you!