Machine learning at scale by Amy Unruh from Google

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Presented at AI NEXTCon Seattle 1/17-20, 2018 http://aisea18.xnextcon.com join our free online AI group with 50,000+ tech engineers to learn and practice AI technology, including: latest AI news, tech articles/blogs, tech talks, tutorial videos, and hands-on workshop/codelabs, on machine learning, deep learning, data science, etc..

Technologie

Confidential & Proprietary
Machine Learning
at Scale
Amy Unruh
Google
@amygdala
AI NEXTCon, 18th Jan 2018

Confidential & Proprietary
What this talk is about…
(Cloud AutoML Vision!)

Google Cloud Platform 3
ML on Google
Cloud Platform
(GCP)

As it’s looked until
recently...
The GCP ML Spectrum :: Technologies

Google Cloud Platform 9
Cloud ML APIs
https://cloud.google.com/products/machine-learning/
(Visit their landing pages to try them out)

Google Cloud Platform 11
mini-demo (Vision API)

scarps and steep slopes exposed riprap* structures
freshwater swamps and woody vegetation Dataset courtesy of Texas A&M University. See
https://storage.googleapis.com/tamucc_coastline/GooglePermissionForIma
ges_20170119.pdf for details.
*loose stone used to form a foundation for a
breakwater or other structure.

hmmm… maybe I need a model
that’s customized for my
problem domain..

One route — build and train a
TensorFlow
model

Fast, flexible and production-ready
framework
●
●
https://github.com/tensorflow

TensorFlow and the Open-Source Community
Contributors
Commits
Stack Overflow questions answered
Community-submitted GitHub
issues responded to weekly
GitHub Stars
GitHub repositories with
‘TensorFlow’ in the title

The Google Brain Team — Looking Back on 2017 (Part 1 of 2)
The Google Brain Team — Looking Back on 2017 (Part 2 of 2)
Eager Execution: An imperative, define-by-run interface to TensorFlow
https://opensource.com/article/17/11/intro-tensorflow

https://github.com/tensorflow/models
+ model weights for transfer learning
The Google Brain Team — Looking Back on 2017 (Part 1 of 2)
The Google Brain Team — Looking Back on 2017 (Part 2 of 2)
Eager Execution: An imperative, define-by-run interface to TensorFlow
https://opensource.com/article/17/11/intro-tensorflow

The GCP ML Spectrum :: Technologies
(and other ML
frameworks on GCP)

The GCP ML Spectrum :: Technologies
Introducing Kubeflow - A Composable, Portable,
Scalable ML Stack Built for Kubernetes

Google Cloud Platform 24
ML at Google scale:
Large scale training and
prediction
with Cloud ML Engine

ML Engine
Fully managed
Distributed Training
scalable prediction
Hyperparameter tuning HyperTune
cloud.google.com/ml-engine
+ Online prediction alpha for
scikit-learn + XGBoost:
https://goo.gl/HabX5z

What resources do you need to solve an ML problem?
Training
data
Model
code
Serving
infrastructure
Prediction
code
Time

Machine Learning as an API
Training
data
Model
code
Serving
infrastructure
Prediction
code
Time

Custom model: build from scratch
Training
data
Model
code
Serving
infrastructure
Prediction
code
Time

Custom model: transfer learning
Training
data
Model
code
Serving
infrastructure
Prediction
code
Time

Google Cloud Platform 31
Introducing
Cloud AutoML Vision

AutoML Vision
Photo dataset
Train Deploy Serve
Generate predictions
with a REST API
AutoML to the rescue

Cloud AutoML Vision
Training
data
Model
code
Serving
infrastructure
Prediction
code
Time

Google Cloud Platform 35
Demo!
(The dataset used is courtesy of Texas A&M University. See
https://storage.googleapis.com/tamucc_coastline/GooglePermissionForImages_20170119.pdf
for details).

Confidence threshold
curves and AUC
stats

(Hmm… should this be a
multi-labeled dataset?..)

Google Cloud Platform 47
Under the hood:
Transfer learning and
Neural Architecture Search

Transfer learning:
a powerful technique to build highly
accurate models even with limited data

For more:
https://cloud.google.com/automl/
To sign up:
Cloud AutoML Alpha Program (https://goo.gl/NjQ8Ss)
Read more:
https://www.blog.google/topics/google-cloud/cloud-automl-makin
g-ai-accessible-every-business/
https://www.nytimes.com/2018/01/17/technology/google-sells-ai.
html
These slides: https://goo.gl/S6qt3k

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2. Confidential & Proprietary Machine Learning at Scale Amy Unruh Google @amygdala AI NEXTCon, 18th Jan 2018

3. Confidential & Proprietary What this talk is about… (Cloud AutoML Vision!)

4. Google Cloud Platform 3 ML on Google Cloud Platform (GCP)

6. The Datacenter as a Computer

7. microsec

8. 2nd generation Tensor Processing Unit

9. As it’s looked until recently... The GCP ML Spectrum :: Technologies

10. Google Cloud Platform 9 Cloud ML APIs https://cloud.google.com/products/machine-learning/ (Visit their landing pages to try them out)

11.

12. Google Cloud Platform 11 mini-demo (Vision API)

13. scarps and steep slopes exposed riprap* structures freshwater swamps and woody vegetation Dataset courtesy of Texas A&M University. See https://storage.googleapis.com/tamucc_coastline/GooglePermissionForIma ges_20170119.pdf for details. *loose stone used to form a foundation for a breakwater or other structure.

14. hmmm… maybe I need a model that’s customized for my problem domain..

15. One route — build and train a TensorFlow model

16. Fast, flexible and production-ready framework ● ● https://github.com/tensorflow

17. TensorFlow and the Open-Source Community Contributors Commits Stack Overflow questions answered Community-submitted GitHub issues responded to weekly GitHub Stars GitHub repositories with ‘TensorFlow’ in the title

18. The Google Brain Team — Looking Back on 2017 (Part 1 of 2) The Google Brain Team — Looking Back on 2017 (Part 2 of 2) Eager Execution: An imperative, define-by-run interface to TensorFlow https://opensource.com/article/17/11/intro-tensorflow

19. https://github.com/tensorflow/models + model weights for transfer learning The Google Brain Team — Looking Back on 2017 (Part 1 of 2) The Google Brain Team — Looking Back on 2017 (Part 2 of 2) Eager Execution: An imperative, define-by-run interface to TensorFlow https://opensource.com/article/17/11/intro-tensorflow

20. So… how to scale out TensorFlow?

21. As it’s looked until recently... The GCP ML Spectrum :: Technologies

22. The GCP ML Spectrum :: Technologies (and other ML frameworks on GCP)

23. The GCP ML Spectrum :: Technologies Introducing Kubeflow - A Composable, Portable, Scalable ML Stack Built for Kubernetes

24. The ML Spectrum :: Technologies

25. Google Cloud Platform 24 ML at Google scale: Large scale training and prediction with Cloud ML Engine

26. ML Engine Fully managed Distributed Training scalable prediction Hyperparameter tuning HyperTune cloud.google.com/ml-engine + Online prediction alpha for scikit-learn + XGBoost: https://goo.gl/HabX5z

27. What resources do you need to solve an ML problem? Training data Model code Serving infrastructure Prediction code Time

28. Machine Learning as an API Training data Model code Serving infrastructure Prediction code Time

29. Custom model: build from scratch Training data Model code Serving infrastructure Prediction code Time

30. Custom model: transfer learning Training data Model code Serving infrastructure Prediction code Time

31. The GCP ML Spectrum :: Technologies

32. Google Cloud Platform 31 Introducing Cloud AutoML Vision

33. AutoML Vision Photo dataset Train Deploy Serve Generate predictions with a REST API AutoML to the rescue

34. Cloud AutoML Vision Training data Model code Serving infrastructure Prediction code Time

35. The ML Spectrum :: Technologies

36. Google Cloud Platform 35 Demo! (The dataset used is courtesy of Texas A&M University. See https://storage.googleapis.com/tamucc_coastline/GooglePermissionForImages_20170119.pdf for details).

37. Preview image labels

38. Label statistics

39. Apply labels to images

40. Train a model for a dataset

41. Confidence threshold curves and AUC stats

42.

43.

44.

45.

46.

47. (Hmm… should this be a multi-labeled dataset?..)

48. Google Cloud Platform 47 Under the hood: Transfer learning and Neural Architecture Search

49. Transfer learning: a powerful technique to build highly accurate models even with limited data

50. For more: https://cloud.google.com/automl/ To sign up: Cloud AutoML Alpha Program (https://goo.gl/NjQ8Ss) Read more: https://www.blog.google/topics/google-cloud/cloud-automl-makin g-ai-accessible-every-business/ https://www.nytimes.com/2018/01/17/technology/google-sells-ai. html These slides: https://goo.gl/S6qt3k

51. Thank you!

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