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Apache PredictionIO - Machine Learning 15minutes! #12

Apache PredictionIO - Machine Learning 15minutes! #12

2017/05/27に開催された第12回 Machine Learning 15minutes! でのLT資料です。
「Apache PredictionIOのコミッタが語る Spark&Elasticsearch 機械学習基盤 」

3ea093e99fbc5ecf5b898c4c0ffd86c0?s=128

takahiro-hagino

May 29, 2017
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  1. Open Source Machine Learning Server 15 Machine Learning minutes!

  2. Apache PredictionIOͷίϛολ͕ޠΔ Spark&Elasticsearch ػցֶशج൫

  3. ػցֶशͷ͓೰Έ • ֶशɾϞσϧσʔλͷετϨʔδ • ػցֶशͷ෼ࢄॲཧϑϨʔϜϫʔΫ • σʔλ༧ଌͷWebαʔϏεʢAPIʣԽ ղܾͷબ୒ࢶ → ఻͍͑ͨ͜ͱ

  4. ػցֶशͷ͓೰Έ • ֶशɾϞσϧσʔλͷετϨʔδ • ػցֶशͷ෼ࢄॲཧϑϨʔϜϫʔΫ • σʔλ༧ଌͷWebαʔϏεʢAPIʣԽ ղܾͷબ୒ࢶ → ఻͍͑ͨ͜ͱ

  5. Photo by Bernard Spragg. NZ About me

  6. Profile Takahiro Hagino Bizreach גࣜձࣾϏζϦʔν • ٻਓݕࡧΤϯδϯʮελϯόΠʯ • AIࣨ

  7. Open Source Machine Learning Server

  8. ϏζϦʔνͰ͸ػցֶशΞϓϦέʔγϣϯͷ ։ൃɾӡ༻ج൫ͱͯ͠ "QBDIF1SFEJDUJPO*0ʹऔΓ૊ΜͰ͍·͢ɻ ೥݄ฐ͔ࣾΒ໊͕ίϛολͱͯ͠ 1SFEJDUJPO*0ͷ։ൃʹࢀՃ͢Δ͜ͱʹͳΓ·ͨ͠ɻ Shinsuke Sugaya Naoki
 Takezoe Takako


    Shimamoto Takahiro Hagino
  9. ϏζϦʔνͰ͸ػցֶशΞϓϦέʔγϣϯͷ ։ൃɾӡ༻ج൫ͱͯ͠ "QBDIF1SFEJDUJPO*0ʹऔΓ૊ΜͰ͍·͢ɻ ೥݄ฐ͔ࣾΒ໊͕ίϛολͱͯ͠ 1SFEJDUJPO*0ͷ։ൃʹࢀՃ͢Δ͜ͱʹͳΓ·ͨ͠ɻ Shinsuke Sugaya Naoki
 Takezoe Takako


    Shimamoto Takahiro Hagino
  10. None
  11. x ML ٻ৬ऀͱٻਓͷϚονϯά Search Quality and Recommendation ೥ऩਪఆ Salary Prediction

    ۀछɾ৬छਪఆ Job Category Prediction ٻਓಛ௃ਪఆ Prediction of Job Characteristics
  12. ٻ৬ऀͱٻਓͷϚονϯά Search Quality and Recommendation ೥ऩਪఆ Salary Prediction ۀछɾ৬छਪఆ Job

    Category Prediction ٻਓಛ௃ਪఆ Prediction of Job Characteristics x ML
  13. ٻ৬ऀͱٻਓͷϚονϯά Search Quality and Recommendation ೥ऩਪఆ Salary Prediction ۀछɾ৬छਪఆ Job

    Category Prediction ٻਓಛ௃ਪఆ Prediction of Job Characteristics x ML
  14. ٻ৬ऀͱٻਓͷϚονϯά Search Quality and Recommendation ೥ऩਪఆ Salary Prediction ۀछɾ৬छਪఆ Job

    Category Prediction ٻਓಛ௃ਪఆ Prediction of Job Characteristics x ML
  15. ٻ৬ऀͱٻਓͷϚονϯά Search Quality and Recommendation ೥ऩਪఆ Salary Prediction ۀछɾ৬छਪఆ Job

    Category Prediction ٻਓಛ௃ਪఆ Prediction of Job Characteristics x ML
  16. ٻ৬ऀͱٻਓͷϚονϯά Search Quality and Recommendation ೥ऩਪఆ Salary Prediction ۀछɾ৬छਪఆ Job

    Category Prediction ٻਓಛ௃ਪఆ Prediction of Job Characteristics x ML
  17. ٻ৬ऀͱٻਓͷϚονϯά Search Quality and Recommendation ೥ऩਪఆ Salary Prediction ۀछɾ৬छਪఆ Job

    Category Prediction ٻਓಛ௃ਪఆ Prediction of Job Characteristics x ML
  18. τϨʔχϯάσʔλ σʔλ͝ͱES΁ͷόονΠϯϙʔτεΫϦϓτΛ४උ͍ͯͨ͠
 ֶशͷ؀ڥ΍࣮ߦϑϩʔ͕୲౰ऀ͝ͱʹଐਓԽ͓ͯ͠Γɺ
 τϨʔχϯάσʔλͷϑΥʔϚοτ΋ఆ·͍ͬͯ·ͤΜɻ ֶशॲཧͷ࣮ߦ࣌ؒ σʔλྔͷ૿Ճʹͱ΋ͳ͏ֶशͷ௕࣌ؒԽ x MLͷ͓ͳ΍Έ

  19. τϨʔχϯάσʔλ σʔλ͝ͱES΁ͷόονΠϯϙʔτεΫϦϓτΛ४උ͍ͯͨ͠
 ֶशͷ؀ڥ΍࣮ߦϑϩʔ͕୲౰ऀ͝ͱʹଐਓԽ͓ͯ͠Γɺ
 τϨʔχϯάσʔλͷϑΥʔϚοτ΋ఆ·͍ͬͯ·ͤΜɻ ֶशॲཧͷ࣮ߦ࣌ؒ σʔλྔͷ૿Ճʹͱ΋ͳ͏ֶशͷ௕࣌ؒԽ x MLͷ͓ͳ΍Έ

  20. ֶशϞσϧͷετϨʔδ ػೳ͝ͱʹֶशϞσϧͷอଘઌ΋ଐਓԽ ༧ଌͷWeb API ػೳ͝ͱʹTornadoͳͲͰ؆қͳAPIαʔόΛ࡞੒͓ͯ͠Γɺ
 ͢͹΍͘ΞϓϦʹ൓өͰ͖ͳ͍ ༧ଌ݁ՌΛฦ͢APIͱͯ͠ESΛར༻͢ΔͳͲ͍ͯͨ͠ x MLͷ͓ͳ΍Έ

  21. ֶशϞσϧͷετϨʔδ ػೳ͝ͱʹֶशϞσϧͷอଘઌ΋ଐਓԽ ༧ଌͷWeb API ػೳ͝ͱʹTornadoͳͲͰ؆қͳAPIαʔόΛ࡞੒͓ͯ͠Γɺ
 ͢͹΍͘ΞϓϦʹ൓өͰ͖ͳ͍ ༧ଌ݁ՌΛฦ͢APIͱͯ͠ESΛར༻͢ΔͳͲ͍ͯͨ͠ x MLͷ͓ͳ΍Έ

  22. AIࣨͷ͓ͳ΍Έ શࣾͷ༷ʑͳࣄۀͱ࿈ܞ ෳ਺ͷࣄۀ͕͋ΓɺػցֶशͷػೳΛఏڙ͍ͯ͠Δ ֤ࣄۀͱσʔλ࿈ܞͷI/FΛڞ௨Խ͍ͨ͠ ։ൃϑϩʔΛڞ௨Խ͍ͨ͠ ઐ೚ͷΠϯϑϥΤϯδχΞ΋͍ͳ͍ ఏڙ͢Δػೳ͝ͱʹɺݸผʹΠϯϑϥΛ੔͑Δͱແବ͕ଟ͍ ग़དྷΔݶΓϑϨʔϜϫʔΫԽ͍ͨ͠

  23. AIࣨͷ͓ͳ΍Έ શࣾͷ༷ʑͳࣄۀͱ࿈ܞ ෳ਺ͷࣄۀ͕͋ΓɺػցֶशͷػೳΛఏڙ͍ͯ͠Δ ֤ࣄۀͱσʔλ࿈ܞͷI/FΛڞ௨Խ͍ͨ͠ ։ൃϑϩʔΛڞ௨Խ͍ͨ͠ ઐ೚ͷΠϯϑϥΤϯδχΞ΋͍ͳ͍ ఏڙ͢Δػೳ͝ͱʹɺݸผʹΠϯϑϥΛ੔͑Δͱແବ͕ଟ͍ ग़དྷΔݶΓϑϨʔϜϫʔΫԽ͍ͨ͠

  24. Solution ղܾࡦΛ୳͠·ͨ͠

  25. None
  26. 4 3FETIJGU 3%4ͷσʔλ͔Β ֶशϞσϧΛ࡞੒Ͱ͖Δ ༧ଌͷͨΊʹΫΤϦΛ࣮ߦͰ͖Δ ֶशϞσϧ͔Β༧ଌ஋Λฦ͢ 8FCΞϓϦέʔγϣϯΛࣗಈੜ੒

  27. Solution

  28. Solution Machine Leaning as a Service ML Tools

  29. Solution Machine Leaning as a Service ML Tools Open Source

  30. Machine Learning Stacks Apps Algorithm Processing Datastore API Server (Tornado…)

    Scikitlearn, SparkML … DL: Caffe2, DL4j, Tensorflow, Chainer … Hadoop, Spark, Storm … Elasticsearch, HBASE, Redshift …
  31. Machine Learning Stacks Apps Algorithm Processing Datastore API Server (Tornado…)

    Scikitlearn, SparkML … DL: Caffe2, DL4j, Tensorflow, Chainer … Hadoop, Spark, Storm … Elasticsearch, HBASE, Redshift … PredictionIO
  32. None
  33. PredictionIO?

  34. Salesforce Acquires PredictionIO

  35. Salesforce Acquires PredictionIO Feb 19, 2016 - TechCrunch 4BMFTGPSDF͕ػցֶशϓϥοτϑΥʔϜͷ 1SFEJDUJPO*0Λങऩ

  36. Salesforce Introduces Salesforce Einstein

  37. Salesforce Introduces Salesforce Einstein Sep 18, 2016 - TechCrunch "*ΛऔΓࠐΉ4BMFTGPSDFͷ໺๬

    ػցֶशϓϥοτϑΥʔϜʮ&JOTUFJOʯΛൃද
  38. The most stars repositories on Github? spark apache/spark ˒ 12.8k

    incubator-predictionio apache/incubator-predictionio ˒ 10.2k playframework playframework/playframework ˒ 9.3k scala scala/scala ˒ 8.2k
  39. spark apache/spark ˒ 12.8k incubator-predictionio apache/incubator-predictionio ˒ 10.2k playframework playframework/playframework

    ˒ 9.3k scala scala/scala ˒ 8.2k The most stars repositories on Github? ˒10.2k
  40. What is PredictionIO?

  41. Apache PredictionIO? Apache PredictionIO (incubating) is an open source Machine

    Learning Server built on top of state-of-the-art open source stack for developers and data scientists create predictive engines for any machine learning task.
  42. Apache PredictionIO (incubating) is an open source Machine Learning Server

    built on top of state-of-the-art open source stack for developers and data scientists create predictive engines for any machine learning task. Apache PredictionIO? ࠷ઌ୺ͷΦʔϓϯιʔεΛ ૊߹Θͤͨػցֶशαʔό
  43. Apache PredictionIO (incubating) is an open source Machine Learning Server

    built on top of state-of-the-art open source stack for developers and data scientists create predictive engines for any machine learning task. Apache PredictionIO? ࠷ઌ୺ͷΦʔϓϯιʔεΛ ૊߹Θͤͨػցֶशαʔό ͲΜͳػցֶशλεΫͰ΋ ༧ଌΤϯδϯ͕ͭ͘ΕΔ
  44. Apache PredictionIO let you ର৅໰୊͝ͱʹςϯϓϨʔτΛ࡞Γɺ
 ͙͢ʹσϓϩΠͰ͖Δ quickly build and deploy

    an engine as a web service on production with customizable templates; ΫΤϦ౤͛ͯ݁ՌΛฦ͢API͕͋Δ respond to dynamic queries in real-time once deployed as a web service;
  45. Apache PredictionIO let you ޡࠩͷௐ੔΍ɺධՁͷ࢓૊Έ΋͋Δ evaluate and tune multiple engine

    variants systematically; όον or ϦΞϧλΠϜͰ
 ֶशσʔλΛొ࿥͢ΔI/F͕͋Δ unify data from multiple platforms in batch or in real-time for comprehensive predictive analytics;
  46. None
  47. None
  48. None
  49. REST: EventAPI SDK: EventClient

  50. None
  51. Engine Template

  52. None
  53. Photo by Bernard Spragg. NZ Quick Start

  54. Versions Latest Release Version v0.11.0

  55. Quick Startʢ೔ຊޠʣ takezoe.hatenablog.com/entry/2017/05/11/132410

  56. Photo by Bernard Spragg. NZ Algorithm

  57. None
  58. Photo by Bernard Spragg. NZ System Architecture

  59. System Architecture Apache Hadoop up to 2.7.2 required only if

    YARN and HDFS are needed
 Apache HBase up to 1.2.4 Apache Spark up to 1.6.3
 for Hadoop 2.6 not Spark 2.x version Elasticsearch up to 1.7.5 not the Elasticsearch 2.x version
  60. Storage roles Meta Data Event Data Model Data ✓ ✓

    ✓ ✓ ✓* ✓ ✓ LOCALFS ✓
  61. Photo by Bernard Spragg. NZ Implementation

  62. Scala ੡ ɾ ػցֶशج൫ PredictionIOͱ SparkʹΑΔϨίϝϯυγ ες Ϝ

  63. x ML

  64. System Requirements ελϯόΠͷϨίϝϯυཁ݅ ϢʔβͷΫϦοΫϩά ͓ؾʹೖΓ௥Ճϩά S3ʹϩάσʔλ্͕͕͍ͬͯΔ ֶश͸೔࣍Ͱ

  65. ElasticsearchͱTasteϓϥάΠϯͰ ࡞ΔϨίϝϯυγεςϜ

  66. PIOಋೖલ σʔλ༻ͷESΠϯσοΫΛຖճ࡞੒ σʔλΠϯϙʔτ༻ͷઐ༻εΫϦϓτ Elasticsearch TasteϓϥάΠϯͰ࣮ߦ ศར͕ͩ൚༻ੑɺσʔλ૿ʹΑΔ࣮ߦ͕࣌ؒ՝୊ʹ ֶश݁ՌΛόϧΫϑΝΠϧͱͯ͠ग़ྗ Elasticsearch ༧ଌͷAPIͱͯ͠ར༻ ࣮ߦϑϩʔΛγΣϧεΫϦϓτͰ؅ཧ

  67. PIOಋೖલ σʔλ༻ͷESΠϯσοΫΛຖճ࡞੒ σʔλΠϯϙʔτ༻ͷઐ༻εΫϦϓτ Elasticsearch TasteϓϥάΠϯͰ࣮ߦ ศར͕ͩ൚༻ੑɺσʔλ૿ʹΑΔ࣮ߦ͕࣌ؒ՝୊ʹ ֶश݁ՌΛόϧΫϑΝΠϧͱͯ͠ग़ྗ Elasticsearch ༧ଌͷAPIͱͯ͠ར༻ ࣮ߦϑϩʔΛγΣϧεΫϦϓτͰ؅ཧ

  68. Click Log Favorite Log Event Server ALS Template

  69. Click Log Favorite Log Event Server ALS Template pio import

  70. Click Log Favorite Log Elasticsearch v5.3 cluster Event Server ALS

    Template pio import Data
  71. Click Log Favorite Log Elasticsearch v5.3 cluster Event Server ALS

    Template pio import Data Spark 2 node cluster RDD
  72. Click Log Favorite Log Elasticsearch v5.3 cluster Event Server ALS

    Template pio import Data LOCALFS Spark 2 node cluster RDD Model
  73. Click Log Favorite Log Elasticsearch v5.3 cluster Event Server ALS

    Template pio import Data LOCALFS Spark 2 node cluster RDD Model Query Predicted Result
  74. Engine Template?

  75. Engine Template

  76. D A S E D-A-S-E Data Source and Data Preparator

    Algorithm Serving Evaluation Metrics
  77. Machine Learning Flow τϨʔχϯάσʔλ Training Data ػցֶशΞϧΰϦζϜ Machine Learning Algorithm

    ༧ଌϞσϧ Predictive Model લॲཧ Preprocessing Πϯϓοτσʔλ Input Data ༧ଌॲཧ Prediction Server ༧ଌ݁Ռ Predicted Result
  78. Machine Learning Flow τϨʔχϯάσʔλ Training Data ػցֶशΞϧΰϦζϜ Machine Learning Algorithm

    ༧ଌϞσϧ Predictive Model લॲཧ Preprocessing Πϯϓοτσʔλ Input Data ༧ଌॲཧ Prediction Server ༧ଌ݁Ռ Predicted Result
  79. Machine Learning Flow τϨʔχϯάσʔλ Training Data ػցֶशΞϧΰϦζϜ Machine Learning Algorithm

    ༧ଌϞσϧ Predictive Model લॲཧ Preprocessing Πϯϓοτσʔλ Input Data ༧ଌॲཧ Prediction Server ༧ଌ݁Ռ Predicted Result
  80. Machine Learning Flow τϨʔχϯάσʔλ Training Data ػցֶशΞϧΰϦζϜ Machine Learning Algorithm

    ༧ଌϞσϧ Predictive Model લॲཧ Preprocessing Πϯϓοτσʔλ Input Data ༧ଌॲཧ Prediction Server ༧ଌ݁Ռ Predicted Result
  81. Machine Learning Flow τϨʔχϯάσʔλ Training Data ػցֶशΞϧΰϦζϜ Machine Learning Algorithm

    ༧ଌϞσϧ Predictive Model લॲཧ Preprocessing Πϯϓοτσʔλ Input Data ༧ଌॲཧ Prediction Server ༧ଌ݁Ռ Predicted Result
  82. Machine Learning Flow τϨʔχϯάσʔλ Training Data ػցֶशΞϧΰϦζϜ Machine Learning Algorithm

    ༧ଌϞσϧ Predictive Model લॲཧ Preprocessing Πϯϓοτσʔλ Input Data ༧ଌॲཧ Prediction Server ༧ଌ݁Ռ Predicted Result
  83. Machine Learning Flow τϨʔχϯάσʔλ Training Data ػցֶशΞϧΰϦζϜ Machine Learning Algorithm

    ༧ଌϞσϧ Predictive Model લॲཧ Preprocessing Πϯϓοτσʔλ Input Data ༧ଌϞσϧ Predictive Model ༧ଌ݁Ռ Predicted Result Data Source & Preparator D
  84. Machine Learning Flow τϨʔχϯάσʔλ Training Data ػցֶशΞϧΰϦζϜ Machine Learning Algorithm

    ༧ଌϞσϧ Predictive Model લॲཧ Preprocessing Πϯϓοτσʔλ Input Data ༧ଌϞσϧ Predictive Model ༧ଌ݁Ռ Predicted Result Data Source & Preparator D Algorithm A
  85. Machine Learning Flow τϨʔχϯάσʔλ Training Data ػցֶशΞϧΰϦζϜ Machine Learning Algorithm

    ༧ଌϞσϧ Predictive Model લॲཧ Preprocessing Πϯϓοτσʔλ Input Data ༧ଌϞσϧ Predictive Model ༧ଌ݁Ռ Predicted Result Data Source & Preparator D Algorithm A Serving S
  86. Machine Learning Flow τϨʔχϯάσʔλ Training Data ػցֶशΞϧΰϦζϜ Machine Learning Algorithm

    ༧ଌϞσϧ Predictive Model લॲཧ Preprocessing Πϯϓοτσʔλ Input Data ༧ଌϞσϧ Predictive Model ༧ଌ݁Ռ Predicted Result Data Source & Preparator D Algorithm A Serving S E Evaluation Metrics
  87. None
  88. D

  89. D A

  90. None
  91. Machine Learning Flow τϨʔχϯάσʔλ Training Data ػցֶशΞϧΰϦζϜ Machine Learning Algorithm

    ༧ଌϞσϧ Predictive Model લॲཧ Preprocessing Πϯϓοτσʔλ Input Data ༧ଌϞσϧ Predictive Model ༧ଌ݁Ռ Predicted Result Data Source & Preparator D Algorithm A Serving S E Evaluation Metrics
  92. D

  93. DataSource •Event Store (Event Server) ͔ΒσʔλΛಡࠐ •TrainingDataΛฦ͢

  94. None
  95. None
  96. D

  97. Preparator • TrainingDataʹର͢Δલॲཧ • ಛ௃நग़ • ෳ਺AlgorithmΛར༻͢Δ৔߹ͷڞ௨ॲཧ • PreparedDataʹม׵ͯ͠Algoritmʹ౉͢

  98. None
  99. None
  100. Machine Learning Flow τϨʔχϯάσʔλ Training Data ػցֶशΞϧΰϦζϜ Machine Learning Algorithm

    ༧ଌϞσϧ Predictive Model લॲཧ Preprocessing Πϯϓοτσʔλ Input Data ༧ଌϞσϧ Predictive Model ༧ଌ݁Ռ Predicted Result Data Source & Preparator D Algorithm A Serving S E Evaluation Metrics
  101. A

  102. Algorithm • train() Λ࣮૷ • ༧ଌϞσϧͷֶशΛ୲౰͢Δ • pio train ίϚϯυͰݺͼग़͞ΕΔ

    • HDFSʢLocalFSʣʹετΞ͞ΕΔ • predict() Λ࣮૷ • σϓϩΠޙͷΫΤϦʹରͯ͠ϦΞϧλΠϜʹݺ͹ΕΔ
  103. None
  104. None
  105. None
  106. None
  107. None
  108. Machine Learning Flow τϨʔχϯάσʔλ Training Data ػցֶशΞϧΰϦζϜ Machine Learning Algorithm

    ༧ଌϞσϧ Predictive Model લॲཧ Preprocessing Πϯϓοτσʔλ Input Data ༧ଌϞσϧ Predictive Model ༧ଌ݁Ռ Predicted Result Data Source & Preparator D Algorithm A Serving S E Evaluation Metrics
  109. None
  110. Serving • LServeΛܧঝ • serve() Λ࣮૷

  111. None
  112. Machine Learning Flow τϨʔχϯάσʔλ Training Data ػցֶशΞϧΰϦζϜ Machine Learning Algorithm

    ༧ଌϞσϧ Predictive Model લॲཧ Preprocessing Πϯϓοτσʔλ Input Data ༧ଌϞσϧ Predictive Model ༧ଌ݁Ռ Predicted Result Data Source & Preparator D Algorithm A Serving S E Evaluation Metrics
  113. Precision@k Precision@5 / Threshold = 2.0 Predicted A ˒ˑˑ Validation

    B ˒˒˒ C ˒˒ˑ D ˑˑˑ E ˒˒ˑ A ˒ˑˑ B ˒˒˒ X ˒˒ˑ D ˑˑˑ E ˒˒ˑ
  114. Precision@k Precision@5 / Threshold = 2.0 Predicted A ˒ˑˑ Validation

    B ˒˒˒ C ˒˒ˑ D ˑˑˑ E ˒˒ˑ A ˒ˑˑ B ˒˒˒ X ˒˒ˑ D ˑˑˑ E ˒˒ˑ
  115. Precision@k Precision@5 / Threshold = 2.0 Predicted A ˒ˑˑ Validation

    B ˒˒˒ C ˒˒ˑ D ˑˑˑ E ˒˒ˑ A ˒ˑˑ B ˒˒˒ X ˒˒ˑ D ˑˑˑ E ˒˒ˑ PositiveCount: 2.0
  116. x ML 5 Jobs

  117. None
  118. None
  119. Photo by Bernard Spragg. NZ Conclusion

  120. τϨʔχϯάσʔλ ϦΞϧλΠϜͰ΋ɺόονͰ΋σʔλΛऔΓࠐΉ͜ͱ͕Ͱ͖Δ ΞΫηετʔΫϯΛൃߦͰ͖ΔͷͰɺ֤αʔϏεͱͷ࿈ܞ͕ศར Elasticsearchͷ෼ࢄετϨʔδͷػೳΛڗडͰ͖Δ ֶशॲཧͷ࣮ߦ࣌ؒ SparkͷΫϥελΛ࢖͏ͨΊɺॲཧΛ෼ࢄֶ͠शʹ͔͔Δ࣌ؒΛ୹ॖ Open Source Machine Learning

    Server
  121. ֶशϞσϧͷετϨʔδ ελϯόΠͰ͸LOCALFSΛར༻͍ͯ͠Δ
 ϞσϧͷಛੑʹԠͯ͡HDFSΛબ୒Մೳ ༧ଌͷWeb API “pio deploy” ίϚϯυ͚ͩͰ༧ଌͷAPIΛ࡞੒Ͱ͖Δ APIαʔό͸Akka-Httpϕʔε Open

    Source Machine Learning Server
  122. ୤ɾଐਓԽ

  123. Photo by Bernard Spragg. NZ Appendix

  124. ϏζϦʔνͰ͸ػցֶशΞϓϦέʔγϣϯΛ ૊৫తʹ։ൃ͍ͯ͘͠ʹ͋ͨΓɺ։ൃɾӡ༻ج൫ͱ ͯ͠"QBDIF1SFEJDUJPO*0ʹऔΓ૊ΜͰ͍·͢ɻ ೥݄ฐ͔ࣾΒ໊͕ίϛολͱͯ͠ 1SFEJDUJPO*0ͷ։ൃʹࢀՃ͢Δ͜ͱʹͳΓ·ͨ͠ɻ Shinsuke Sugaya Naoki
 Takezoe Takako


    Shimamoto Takahiro Hagino
  125. ϏζϦʔνͰ͸ػցֶशΞϓϦέʔγϣϯΛ ૊৫తʹ։ൃ͍ͯ͘͠ʹ͋ͨΓɺ։ൃɾӡ༻ج൫ͱ ͯ͠"QBDIF1SFEJDUJPO*0ʹऔΓ૊ΜͰ͍·͢ɻ ೥݄ฐ͔ࣾΒ໊͕ίϛολͱͯ͠ 1SFEJDUJPO*0ͷ։ൃʹࢀՃ͢Δ͜ͱʹͳΓ·ͨ͠ɻ Shinsuke Sugaya Naoki
 Takezoe Takako


    Shimamoto Takahiro Hagino
  126. How to Contribute to PIO

  127. Add support for Elasticsearch 5.x

  128. None
  129. jpioug.org

  130. • ೔ຊ Apache PredictionIO Ϣʔβձ JPIOUG Join Us!

  131. None
  132. 30 6݄ FRI

  133. 30 6݄ FRI Open Source Machine Learning Server 02 JPIOUG

    Meetup 19:30 @Shibuya
  134. 30 6݄ FRI Open Source Machine Learning Server 02 JPIOUG

    Meetup 19:30 @Shibuya ʲٸืʳLT͍ͨ͠ํ
  135. 30 8݄ WED

  136. 30 8݄ WED Open Source Machine Learning Server 03 JPIOUG

    Meetup 19:30 @Shibuya
  137. 30 8݄ WED Open Source Machine Learning Server 03 JPIOUG

    Meetup 19:30 @Shibuya ʲΏΔืʳLT͍ͨ͠ํ
  138. jpioug.org

  139. Open Source Machine Learning Server 15 Machine Learning minutes! Thank

    You