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
The state of NLP in production 🥽
Search
Abdur-Rahmaan Janhangeer
August 27, 2023
Programming
0
130
The state of NLP in production 🥽
NLP in production vs real life
Abdur-Rahmaan Janhangeer
August 27, 2023
Tweet
Share
More Decks by Abdur-Rahmaan Janhangeer
See All by Abdur-Rahmaan Janhangeer
Building AI Agents with Python: A Deep Dive
osdotsystem
0
24
Extending Flask using the Flask Plugins API
osdotsystem
0
110
PEPs that hit the front page
osdotsystem
0
90
libSQL: Taking Sqlite To The Moon
osdotsystem
0
200
Boosting Python With Rust 🚀
osdotsystem
0
200
Flet: Flutter in Python
osdotsystem
0
430
SQLite Internals: How The World's Most Used Database Works
osdotsystem
2
3.7k
Fast Flask Dev For Big Codebases
osdotsystem
0
220
Python Bytecode or How Python Operates
osdotsystem
0
300
Other Decks in Programming
See All in Programming
Understanding Kotlin Multiplatform
l2hyunwoo
0
260
令和最新版手のひらコンピュータ
koba789
13
7.6k
Constant integer division faster than compiler-generated code
herumi
2
600
あなたとJIT, 今すぐアセンブ ル
sisshiki1969
1
620
抽象化という思考のツール - 理解と活用 - / Abstraction-as-a-Tool-for-Thinking
shin1x1
1
970
「リーダーは意思決定する人」って本当?~ 学びを現場で活かす、リーダー4ヶ月目の試行錯誤 ~
marina1017
0
220
画像コンペでのベースラインモデルの育て方
tattaka
3
1.6k
大規模FlutterプロジェクトのCI実行時間を約8割削減した話
teamlab
PRO
0
470
CEDEC2025 長期運営ゲームをあと10年続けるための0から始める自動テスト ~4000項目を50%自動化し、月1→毎日実行にした3年間~
akatsukigames_tech
0
120
Scale out your Claude Code ~自社専用Agentで10xする開発プロセス~
yukukotani
9
2k
Go製CLIツールをnpmで配布するには
syumai
2
1.2k
Claude Code と OpenAI o3 で メタデータ情報を作る
laket
0
130
Featured
See All Featured
ピンチをチャンスに:未来をつくるプロダクトロードマップ #pmconf2020
aki_iinuma
126
53k
jQuery: Nuts, Bolts and Bling
dougneiner
63
7.8k
Building Adaptive Systems
keathley
43
2.7k
Designing Dashboards & Data Visualisations in Web Apps
destraynor
231
53k
The Power of CSS Pseudo Elements
geoffreycrofte
77
5.9k
Making Projects Easy
brettharned
117
6.3k
Unsuck your backbone
ammeep
671
58k
Why Our Code Smells
bkeepers
PRO
337
57k
Understanding Cognitive Biases in Performance Measurement
bluesmoon
29
1.8k
Practical Tips for Bootstrapping Information Extraction Pipelines
honnibal
PRO
23
1.4k
Facilitating Awesome Meetings
lara
54
6.5k
Why You Should Never Use an ORM
jnunemaker
PRO
58
9.5k
Transcript
The state of NLP in production
None
Python Mauritius Usergroup site fb linkedin mailing list 3
url pymug.com site 4
About me compileralchemy.com 5
slides 6
The state of NLP in production 7
Hardest part of a real-world project 8
? 9
Is it cooking up an awesome model? 10
No, the world is more complex than this 11
Elements of an NLP project 12
NLP project gather data clean store train use model retrain
model 13
gather data 14
Toy project use curated data set quick extraction 15
Real project a lot of data needed data corresponds to
business case. data probably does not exist speed of data gathering find ingenious / better ways of getting data automate collection 16
clean/preprocess data 17
Toy project use an existing parser / curator e.g. NLTK
existing options 18
Real project use a parser intended for it, several custom
steps parallel processing of data 19
store data 20
Toy project laptop 21
Real project cloud database hot / cold data TTL 22
training 23
Toy project use laptop / external GPU 24
Real project on cloud training on cloud knowledge cross-cloud skills
fault tolerance 25
use model 26
Toy project local website / code 27
Real project continuation of pipeline web service architecture devops /
deploy 28
retraining 29
Toy project euhh this even exists???? 30
Real project learn cloud offerings for continuous learning ways to
retrain / fine tune 31
It's more than serving a model 32
Operation model 33
[ pipeline ] data collection --- process --- train -<-
| | --------------------------- model ^ | | | | --->--- V web service [pod] [pod] --- happy user | -> users service [pod] [pod] | -> db service [pod] 34
skills chart 35
skills --------------- --------------- | | | | | backend |
| devops | | | | | --------------- --------------- --------------- --------------- | | | | | backend | | data eng | | | | | --------------- --------------- 36
skills --------------- --------------- | | | | | backend |
| devops | | | | | --------------- --------------- web service deploy --------------- --------------- | | | | | ml | | data eng | | | | | --------------- --------------- models pipelining 37
code blueprint [ architecture repos ] [ pipeline repos ]
[ ml repos ] [ backend repos ] 38
Tools 39
Pandas Good queries Much resources Read SQL 40
Dask Good for it's purpose: Parallelize tasks Poor docs 41
Polars Awesome parallelizations Great docs 42
NLTK use spacy if possible 43
Notebooks great for cloud used in production on the cloud
44
Advice to research / scientists folks keep everything clean people
will come after you always in hurry / messy / i'll clean it later mood good practices? is this phrase in the korean dictionary? 45
General advices have great docs good onboarding have great standards
46
Keep learning! 47