Ready to use Deep Learning Models: All You Need is 5 Minutes

Talk at Data Science GO 2018, San Diego, CA 2018-10-13

- Conference website: https://www.datasciencego.com

You can find other presentations on my website: https://k-roz.com/talks/

Transcript

1. GABRIELA DE QUEIROZ R E A D Y T O U S E D E E P L E A R N I N G M O D E L S : A L L Y O U N E E D I S 5 M I N U T E S SENI OR DEVELOPER ADVOCATE (ML /DL/A I) @ IBM @gdequeiroz | www.k-roz.com | gdq@ibm.com
2. GABRIELA DE QUEIROZ ‣ Sr Developer Advocate, IBM ‣ Founder, R-Ladies ‣ Lead Data Scientist ‣ Data Scientist ‣ Statistician/Epidemiologist/ Researcher About me Data + Community + Mentor + Advocate @gdequeiroz | www.k-roz.com | gdq@ibm.com
3. Center for Open Source Data and AI Technologies (CODAIT) CODAIT aims to make AI solutions easier to create, deploy, and manage in the enterprise Relaunch of the Spark Technology Center (STC) to reflect expanded mission 30+ open source developers! Watson West Building 505 Howard St. San Francisco, California Improving Enterprise AI lifecycle in Open Source Gather Data Analyze Data Machine Learning Deep Learning Deploy Model Maintain Model Python Data Science Stack Fabric for Deep Learning (FfDL) Mleap + PFA Scikit-Learn Pandas Apache Spark Apache Spark Jupyter Model Asset eXchange Keras + Tensorflow CODAIT codait.org
4. Improving Enterprise AI lifecycle in Open Source Gather Data Analyze Data Machine Learning Deep Learning Deploy Model Maintain Model Python Data Science Stack Fabric for Deep Learning (FfDL) Mleap + PFA Scikit-Learn Pandas Apache Spark Apache Spark Jupyter Model Asset eXchange Keras + Tensorflow CODAIT: Enabling End-to-End AI in the Enterprise
5. Have you ever wanted to classify images, recognize faces or places or generate captions of images? @gdequeiroz | www.k-roz.com | gdq@ibm.com
6. With the Model Asset eXchange, you can!
7. The Model Asset eXchange enables domain experts to use deep learning in the enterprise. Q: What is deep learning?
 A: Machine learning using deep neural networks. 
 Q: What is a deep neural network?
 A: A neural network with multiple hidden layers.

8. What is a neural network?
9. ! = #$% + &$' + c$) Linear regression What is a neural network?
10. ! = #$% + &$' + c$) x 1 x 2 x 3 y a b c Linear regression What is a neural network? Nodes Edges
11. What is a neural network? x 1 x 2 x 3 y 3 y 1 y 4 y 2 Multiple linear regressions at the same time
12. What is a neural network? Second layer of linear regressions Multilayer Perceptron Neural Network
13. What is a neural network? Second layer of linear regressions Multilayer Perceptron Neural Network Dense (3×4) Dense (4×2) Input (3) Output (2) Same network in a more compact notation
14. What is a deep neural network? A neural network with multiple hidden layers Dense (3×8) Dense (8×6) Input (3) Output (2) Dense (6×4) Dense (4×2) “toy" deep neural network
15. Applying Deep Learning: Perception Data ??? Train model ??? $$$ Get model ??? Deploy model ??? $$$ Training – Data Scientist Consumption – App Developer, Domain Expert
16. Applying Deep Learning: Reality Find model Get code Test, verify, fix Train model Deploy model Use model Discovery Execution Consumption 1 2 3 4A 4B 5 @gdequeiroz | www.k-roz.com | gdq@ibm.com
17. Step 1: Find a model … that does what you need … that is free to use … that is performant enough
18. Step 2: Get the code Is there a good implementation available? … that does what you need … that is free to use … that is performant enough TensorFlow code to build ResNet50 neural network graph
19. Or … Step 2: Get the pre-trained weights Is there a good pre-trained model available? … that does what you need … that is free to use … that is performant enough
20. Step 3: Verify the model you found Check … … that does what you need … that is free to use (license) … that is performant enough (computation & accuracy)
21. Step 4 (a): Train the model
22. Step 4 (a): Train the model
23. Step 4 (b): Figure out how to deploy your model … adjust inference code (or write from scratch) … package inference code and model code, and pre-trained weigths together … deploy your package
24. Step 5: Consume your model … plug in into your application
25. Step 6: Profit … hopefully
26. Applying Deep Learning: Reality Find model Get code Test, verify, fix Train model Deploy model Use model Discovery Execution Consumption 1 2 3
27. Model Asset eXchange • A one-stop place for developers/data scientists to find and use free and open source deep learning models ibm.biz/model-exchange
28. Model Asset eXchange • Wide variety of domains (text, audio, image, etc) • Multiple deep learning frameworks • Vetted and tested code and IP • Build and deploy a model web service 
 in seconds • Training on Fabric for Deep Learning (FfDL) or Watson Machine Learning in minutes ibm.biz/model-exchange
29. Trainable Models TRAINING DATA TRAINING CODE TRAINING DEFINITION STANDARDIZED SCRIPT https://github.com/IBM/FfDL
30. DATA MODEL COMPUTER RESOURCES EXPERTISE Input/Output Processing Pre-Trained Model REST API Deep Learning Asset on Model Asset Exchange ibm.biz/model-exchange Deployable Models
31. Swagger Specification Deep Learning Asset on Model Asset Exchange ibm.biz/model-exchange Deployable Models Deploy Inference Endpoint Metada Endpoint Microservice REST API
32. OPEN SOURCE & FREE ibm.biz/model-exchange
33. Highlights • Image, audio, text, healthcare, time-series and more • Pre- / post-processing & inference wrapped up in Docker container • Generic API framework code - Flask RESTPlus • Swagger specification for API • One-line deployment locally and on a Kubernetes cluster
34. •Code Patterns demonstrating how to easily consume MAX models ibm.biz/max-developers
35. All that is available for YOU for FREE
36. DEMO! Wish me luck ibm.biz/model-exchange ibm.biz/max-developers
37. There is no failure, only feedback
38. Thank you! MITJA BOSNIČ KIRILL EREMENKO PAULO REALPE SEBASTIAN MONCADA MARÍA VIRGINIA PERDOMO MARTÍN DURÁN RUBY ANGELA PAGALAN
39. Thank you GDQ@IBM,COM K-ROZ.COM @ G D E Q U E I R O Z