Watson Studioで かんたん機械学習モデル作成

2018年12月14日オープンソースカンファレンス2018 OSC2018.Enterpriseでの登壇資料です。

Transcript

1. Watson Studio ) ( Developer Advocate 
    
      2018 .Enterprise 2018-12-14 () 13:00-13:45 @5F 
   5A
2. Kyoko NISHITO 
    Developer Advocate
3. "  3 • &$ Watson Studio   !"(/'- *) #% ., • 
   Watson Studio Model Builder+ (/ '- *) 
4. Watson Studio 4 Model Lifecycle Management c c i c c i c o d vf AI C a c e st • IBM liu (LocalC r ) • cS IBM C n fI • R, Python, Scala iu •
     
    • 
   
    c c i c c i c iu
5. ) ( Learning ( 5 • Watson Studio DF Fabric for Deep Learning (FfDL) • Code Patterns Deploy and use a multi-framework deep learning platform on Kubernetes • Github https://github.com/IBM/FfDL
6. IBM Developer https://developer.ibm.com/jp/ 6 •    140   Code Patterns •    6,000  
   
7. 7  Watson Studio    
     
   
8. 
    8 dog cat cat dog Positive ID: Pug Unknown Unknown Positive ID: Pug dog dog dog dog 
    
9.   (1'0+) 9 dog cat cat dog Positive ID: Pug Unknown Unknown Positive ID: Pug 
    ,/'0 Watson Studio  #$" (1'0 Spark ML*&+) 
   #$%!.- Apache Spark
   
10. 3 ) 10 ) 3( ) 3 ) I 3 ) I 3 i : ) • SEPAL LENGTH: cm • SEPAL WIDTH : cm • PETAL LENGTH: cm • PETAL WIDTH : cm • CLASS: Iris-setosa( ), Iris-versicolour( ) ), Iris-virginica ( )I )
11. %  11    #!   !  #  "$% • SEPAL LENGTH:  
     • SEPAL WIDTH :  #
     • PETAL LENGTH: !  
     • PETAL WIDTH : !  #
     • CLASS : Iris-setosa(), Iris-versicolour(), Iris-virginica ()
12. 12
         
13. Watson . 13 IBM Cloud 2. . Watson Studio ( AI) S/ . 1 Machine Learning ( AI) S/ . 1 Apache Spark ( Web ) S/ . 1 ( )
    
    
14. 1. Watson Studio
     14
    
    
15. 2. Watson Studio 15 1.  
     (IBM Cloud ) 2.
     Watson Studio
      3. Get Started
    
16. 2. Watson Studio  16 4.   5  [Next]( [Done])
      
    
    
    
17. . 17 1. 3 New in the community Explore
    
18. 3 18 2. 3 iris 3. UCI: Iris . .
    
19. . 19 4. 3 , iris.csv
    
20. 20 5. iris.csv 7 iris_test.csv4 setosa, versicolour, virginica . 10 4 3 iris_test.csv 4 3 5iris.csv 7
     4 
21. 4. Project 21 1. [Create a Project] 
    2. [Standard] 
    
    
22. 4. Project 22 1. Name Project 
    2. [Create] 
    
    
23. 23 .
    
24. . 4 24 1. [Settings] 2. Associated services [+Add services] [Spark]
    
25. 25 1. [Existing] .4 . 2. Existing Service Instance 1 Apache Spark 1. [Select] .4 .
    
26. . 26 [Settings] 4. Associated services [+Add services] [Watson] 4 5. Machine Learning [Add]
    
27. 27 6. [Existing] .4 . 7. Existing Service Instance 1 Machine Learning 8. [Select] .4 .
    
28. . 28 9. [Settings] . Associated services .4
    
29. 5 29 1. [0101] . 2. iris.csv 5
    
30. 5 30 1. Files . 2. File .
    
31. 6 31 1. [Assets] . Asset
    
32. . 32 2. Models Watson Machine Learning models [+ New Watson Machine Learning model]
    
33. 6 33 3. [Name] 6 4. Machine Learning Service Machine Learning . 5. Select model type: Model builder 6. Select runtime: Apache Spark . 7. Manual 8. [Create]
    
34. . 34 9. Select data asset iris.csv [Next]
    
35. 35 9. Select Label Col CLASS . 6 6 6
    
36. . 36 10. . .Feature Columns . Label Col . 6 . .
    
37. . 37 11. . . . . . • Binary Classification: (2 . 6 • Multiclass Classification: ( . 6 ) • Regression: ( . ) 3 . . . Multiclass Classification
    
38. . 38 11. . 6. . Train 6 Test 6 Holdout 6( ) .
    
39. . 6 39 12. . [+ Add Estimators]
    
40. 40 13. 6 .[Add] 6
    
41. 41 * Model Builder 
     Binary Classification Multiclass Classification Regression Logistic Regression 6 . Y Decision Tree Regression Y Y Y Random Forest Regression Y Y Y Gradient Boosted Tree Regression 6 Y Y Native Bayes Y Linear Regression Y Isotonic Regression . Y 
42. 6 42 6 . 6 
    
43. 6 43 14. [Next] .
    
44. 65 44 1 .
    
45. 45 1 . F 6 ???? 
    
46. 6 46 17. PERFORMANCE [Save] . .
    
47. . 47 18. Overview
    
48. 48 1. 7 Web . [Deployments]
    
49. . 49 3. [Add Deployments
    
50. . 50 4. Name Deployment type Web service [Save]
    
51. . 51 .
    
52. 8 5 52 5 5. .
    
53. 53 DEMO    
     
      
    
54. 1 . 54 1. Name
    
55. 1 . 55 2. Test
    
56. 56 3. [Predict] iris_test.csv 1 • SEPAL LENGTH : cm • SEPAL WIDTH : cm • PETAL LENGTH : . cm • PETAL WIDTH : . cm • Iris-setosa( ), • Iris-versicolour( ) • Iris-virginica ( ) 
    
57. 14 57 . .
58. . 58 Implementation 2 2 Web cURL, snipet
59. 59 !#  $   " &%    
    
60.  • Watson StudioModel Builder)1(/# % 0.+* • +* # %Web"-'  ,   $!&!,  
61. IBM Cloud!# • %$ '0IBM Cloud!# )3   • "*,
    +&4-5 Watson6 /.API  42 1(  #  https://www.ibm.com/cloud-computing/jp/ja/lite-account/
62. u R U etr ou kta IBM Partners https://ibm.biz/ibmpartners cphbdkik h I E 6 K A (s I E *IIAI E *41 *41 C (m l ,4 ) 5*3 0 K HE I B C – ) 1 3 C B AE 4C HD E HGHAI M A4 https://ibm.biz/buildwith 1,000 IBM Cloud A4 1 12,000l M 132 P S 1 3 ,C K f nid h k T W
63. IBM
    @6F •  "Watson
    0/' • IBM Cloud$,-  ( !
    Zero +TJBot 23#$' • &%  ).*1  
64. 64 Thank you twitter.com/KyokoNishito github.com/kyokonishito developer.ibm.com/jp