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

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/

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Gabriela de Queiroz

October 13, 2018
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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