7 steps to AI production - global azure bootcamp 2020 Koln

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Session: 7 steps to AI production Abstract: What was your last AI project? Was it another Kaggle dataset running on Jupyter notebook, hard to reproduce, and don't know how to deploy as an AI service? How to do auto-scaling for the model serving? How far is the distance from playing with the sample dataset to AI production? Let's go through 7 steps in the AI application development lifecycle. From data wrangling, reproduce your training, model acceptance to model deployment and management. Target audience: Data scientist who doesn't know the model serving and Azure DevOps. Backend/DevOps who doesn't know how to help your data team go production. --- Github: https://github.com/pymia/7-steps-production Event: Global Azure Bootcamp 2020 Virtual Date: Apr 25, 2020 Event link: https://www.meetup.com/Azure-Cologne-Meetup/events/266727986/ Linkedin: http://linkedin.com/in/mia-chang/

Daten & Analysen

Cologne
Virtual
7 steps to AI Production
Mia Chang

Thank you to
our sponsors
Cologne
Virtual

7 steps to AI Production
Mia Chang -
Senior Data Scientist @ Akelius
Microsoft AI MVP
Book Author of
Microsoft AI MVP Book(2019, Amazon Kindle)
AI Using Microsoft Cognitive Services(later 2020, Manning)
Linkedin: https://www.linkedin.com/in/mia-chang/
Twitter: @bymiachang

key takeaway…
01
02
03
04preprocess
data quality
reproducible process
model
serve with ﬂask
docker
mantainess
health check, business logic
decomposition
cloud
integration with azure webapp

2.refactor your code on the jupyter notebook

2.refactor your code on the jupyter notebook
Design Lab at UC San Diego scraped and analyzed over 1 million Jupyter
Notebooks from GitHub. They Found...
https://blog.jupyter.org/we-analyzed-1-million-jupyter-notebooks-now-you-can-too-guest-post-8116a964b536

Hey,
How’s everything goes?
May I check your code on the notebook?
https://images.techhive.com/images/article/2016/09/data_science_classes-100682563-large.3x2.jpg

Um…...
https://images.ctfassets.net/9l3tjzgyn9gr/photo-63973/dc36ca09774f5048684c279bbd978809/63973-embarassbaby.jpg

- new colleague comes
- code review
- checkout my code,
which I committed before
vacation 2 weeks ago...
https://www.123rf.com/photo_82339866_disorder-mess-at-home.html

demo. 1. refactor code as method/class
2. use type hint

demo. 3.serve with flask app
4.dockerize your app

5.deploy your webapp
https://docs.microsoft.com/en-us/azure/app-service/deploy-continuous-deployment#option-1-use-app-service-kudu-build-server

if you are interested to know more…
01
02
03
04
preprocess
data quality
reproducible process
data sampling
model
serve with ﬂask
docker
move the model to the blob storage
mantainess
health check, business logic
decomposition
authentication
cloud
integration with azure webapp
CI/CD with Azure DevOps

7 steps to AI Production
Mia Chang
https://github.com/pymia/7-steps-production

Feedback:
Cologne
Virtual
http://feedback.azurecgn.de/

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The content was modified from Google Content Group Eric ShangKuan(ericsk@google.com) --- TensorFlow Lite guide( for mobile & IoT ) TensorFlow Lite is a set of tools to help developers run TensorFlow models on mobile, embedded, and IoT devices. It enables on-device machine learning inference with low latency and small binary size. TensorFlow Lite consists of two main components: The TensorFlow Lite interpreter: - optimize models on many different hardware types, like mobile phones, embedded Linux devices, and microcontrollers. The TensorFlow Lite converter: - which converts TensorFlow models into an efficient form for use by the interpreter, and can introduce optimizations to improve binary size and performance. --- Event: PyLadies TensorFlow All-Around Date: Sep 25, 2019 Event link: https://www.meetup.com/PyLadies-Berlin/events/264205538/ Linkedin: http://linkedin.com/in/mia-chang/

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1. Cologne Virtual 7 steps to AI Production Mia Chang

2. Thank you to our sponsors Cologne Virtual

3. 7 steps to AI Production Mia Chang - Senior Data Scientist @ Akelius Microsoft AI MVP Book Author of Microsoft AI MVP Book(2019, Amazon Kindle) AI Using Microsoft Cognitive Services(later 2020, Manning) Linkedin: https://www.linkedin.com/in/mia-chang/ Twitter: @bymiachang

4. key takeaway… 01 02 03 04preprocess data quality reproducible process model serve with ﬂask docker mantainess health check, business logic decomposition cloud integration with azure webapp

5. iris dataset

6. 1.data quality: pandas-proﬁling

7. 1.data quality: pandas-proﬁling

8. 1.data quality: pandas-proﬁling

9. 1.data quality: pandas-proﬁling

10. demo.

11. 2.refactor your code on the jupyter notebook

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17. demo. 1. refactor code as method/class 2. use type hint

18. 3.serve with ﬂask app

19. 3.serve with ﬂask app

20. demo. 3.serve with flask app 4.dockerize your app

21. 5.deploy your webapp https://docs.microsoft.com/en-us/azure/app-service/deploy-continuous-deployment#option-1-use-app-service-kudu-build-server

22. 6.health check, testing

23. 6.health check, testing

24. 7. decomposition

25.

26. 7. decomposition RESTful service

27. 7. decomposition

28. key takeaway… 01 02 03 04 preprocess data quality reproducible process model serve with ﬂask docker mantainess health check, business logic decomposition cloud integration with azure webapp

29. if you are interested to know more… 01 02 03 04 preprocess data quality reproducible process data sampling model serve with ﬂask docker move the model to the blob storage mantainess health check, business logic decomposition authentication cloud integration with azure webapp CI/CD with Azure DevOps

30. 7 steps to AI Production Mia Chang https://github.com/pymia/7-steps-production

31. Feedback: Cologne Virtual http://feedback.azurecgn.de/

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