BigML Spring 2016 Release

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WhizzML is a new domain-specific language for automating Machine Learning workflows, implement high-level Machine Learning algorithms, and easily share them with others. WhizzML offers out-of-the-box scalability, abstracts away the complexity of underlying infrastructure, and helps analysts, developers, and scientists reduce the burden of repetitive and time-consuming analytics tasks.

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BigML Spring 2016 Release

  1. 1. BigML Spring 2016 Release
  2. 2. BigML, Inc 2 Spring 2016 Release POUL PETERSEN (CIO) Enter questions into chat box – we’ll answer some via chat; others at the end of the session https://bigml.com/releases ATAKAN CETINSOY, (VP Predictive Applications) Resources Moderator Speaker Contact info@bigml.com Twitter @bigmlcom Questions @whizzml
  3. 3. BigML, Inc 3
  4. 4. BigML, Inc 4 Promise of ML time Want •Reduce churn •Increase conversion •Improve diagnosis •Reduce fraud •Etc. Automated InsightsData Have
  5. 5. BigML, Inc 5 ML Hurdles time •Which algorithms? •How to scale it? •How to handle real data? •How to tune it? •How to automate it?
  6. 6. BigML, Inc 6 Current Resources SOURCE DATASET CORRELATION STATISTICAL TEST MODEL ENSEMBLE LOGISTIC REGRESSION EVALUATION ANOMALY DETECTOR ASSOCIATION DISCOVERY PREDICTION BATCH PREDICTIONSCRIPT LIBRARY EXECUTION Data Exploration Supervised Learning Unsupervised Learning Automation CLUSTER Scoring
  7. 7. BigML, Inc 7 BigML Vision time Automation Paving the Path to Automatic Machine Learning REST  API Programmable   Infrastructure A Sauron   • Automatic  deployment  and   auto-­‐scaling Data  Generation  and   Filtering C Flatline   • DSL  for  transformation  and   new  field  generation B Wintermute   • Distributed  Machine  Learning   Framework   2011 Spring 2016 Automatic  Model   Selection E SMACdown     • Automatic  parameter   optimization Workflow   Automation D WhizzML   • DSL  for  programmable   workflows  
  8. 8. BigML, Inc 8 Workflow Map Decision  Trees   Bagging   Decision  Forest   LogisGc  Regression  MODEL DATASET CLUSTER ANOMALY ASSOCIATION SOURCE K-­‐Means   G-­‐Means   IsolaGon  Forest   Magnum  Opus   StaGsGcal  Tests   CorrelaGons   STATSDATASET Flatline   Flatline  Editor   PREDICTION Batch  PredicGon   Batch  Anomaly   Batch  Centroid   EvaluaGon  
  9. 9. BigML, Inc 9 Original Workflow SOURCE DATASET MODEL PREDICTION
  10. 10. BigML, Inc 10 Regular Workflows MODEL FILTERSOLD HOMES BATCH PREDICTION NEW FEATURES DATASET DEALS DATASET FILTERFORSALE HOMES NEW FEATURES
  11. 11. BigML, Inc 11 Model Selection ENSEMBLE LOGISTIC REGRESSION EVALUATION SOURCE DATASET TRAINING TEST MODEL EVALUATIONEVALUATION CHOOSE
  12. 12. BigML, Inc 12 Model Tuning ENSEMBLE N=20 EVALUATION SOURCE DATASET TRAINING TEST EVALUATIONEVALUATION ENSEMBLE N=10 ENSEMBLE N=1000 CHOOSE
  13. 13. BigML, Inc 13 SMACdown •How many models? •How many nodes? •Missing splits or not? •Number of random candidates? •Balance the objective? SMACdown can tell you!
  14. 14. BigML, Inc 14 Best-First Features {F1} CHOOSE BEST S = {Fa} {F2} {F3} {F4} Fn S+{F1} S+{F2} S+{F3} S+{F4} S+{Fn-1} CHOOSE BEST S = {Fa, Fb} S+{F1} S+{F2} S+{F3} S+{F4} S+{Fn-1} CHOOSE BEST S = {Fa, Fb, Fc}
  15. 15. BigML, Inc 15 Stacked Generalization ENSEMBLE LOGISTIC REGRESSION SOURCE DATASET MODEL BATCH PREDICTION BATCH PREDICTION BATCH PREDICTION EXTENDED DATASET EXTENDED DATASET EXTENDED DATASET LOGISTIC REGRESSION
  16. 16. BigML, Inc 16 Better Algorithms •Stacked Generalization •Boosting •Adaboost • Logitboost •Martingale Boosting •Gradient Boosting
  17. 17. BigML, Inc 17 Why Workflows •Machine Learning is iterative by nature. •ML tools still require many repetitive (and manual) tasks. •Instead of helping to focus on the output many tools force analysts, developers, and scientists to focus on infrastructure, parallelism, etc. •Not everybody can implement complex workflows or meta-algorithms but many people can reuse them.
  18. 18. BigML, Inc 18 WhizzML Features •A Domain-Specific Language (DSL) for automating Machine Learning workflows. •Complete programming language. •Machine Learning “operations” are first-class citizens. •Scale is provided for free. •API First! - Everything is composable.
  19. 19. BigML, Inc 19 WhizzML API Resources SCRIPT LIBRARY EXECUTION
  20. 20. BigML, Inc 20 export BIGML_USERNAME=myuser export BIGML_API_KEY=6ef37b3d791061d345ef51281dae821ac7943ed7 export BIGML_AUTH="username=$BIGML_USERNAME;api_key=$BIGML_API_KEY" export SCRIPT="https://bigml.io/script?$BIGML_AUTH" export LIBRARY="https://bigml.io/library?$BIGML_AUTH" export EXECUTION="https://bigml.io/execution?$BIGML_AUTH" Via API
  21. 21. BigML, Inc 21 Via API http $LIBRARY source_code="(define (addition a b) (+ a b))" | jq ".resource" "library/573a97f5b95b3941f6000004" http $SCRIPT imports:='["library/573a97f5b95b3941f6000004"]' source_code="(addition x 2)" inputs:='[{"name": "x", "type": "number"}]' | jq ".resource" "script/573a9862b95b3941ff000015" http $EXECUTION script=script/573a9862b95b3941ff000015 inputs:='[["x", 5]]' | jq ".resource" "execution/573a987ab95b3941f000000d" http http://bigml.io/execution/573a987ab95b3941f000000d?$BIGML_AUTH | jq ".execution.result" 7
  22. 22. BigML, Inc 22 Via Bindings https://gist.github.com/whizzmler/8a849c282a770ac79a1441df5c5ccf62
  23. 23. BigML, Inc 23 Gallery Scripts UPDATE ME!!!
  24. 24. BigML, Inc 24 Importing from GitHub
  25. 25. BigML, Inc 25 WhizzML in GitHubNEW https://github.com/whizzml/examples
  26. 26. BigML, Inc 26 WhizzML UI Resources
  27. 27. BigML, Inc 27 Script Editor
  28. 28. BigML, Inc 28 WhizzML REPLNEW https://bigml.com/whizzml
  29. 29. BigML, Inc 29 Reify •"Reifies" a resource into a WhizzML script. •Rapid prototyping meets automation. •Coming soon…
  30. 30. BigML, Inc 30 Secret Link Scripts https://bigml.com/shared/script/oazVtg8t2V2JHFf6PLmenUJbNU https://bigml.com/dashboard/script/573d53a628eb3e026f000012
  31. 31. BigML, Inc 31 A Gallery of Scripts https://bigml.com/gallery/scripts
  32. 32. BigML, Inc 32 Share or Sell •Each script is reviewed internally by BigML Team members.
  33. 33. BigML, Inc 33 Personalizing 1-Click Menus
  34. 34. BigML, Inc 34 Demo
  35. 35. BigML, Inc 35 API Documentation • https://bigml.com/developers/libraries • https://bigml.com/developers/scripts • https://bigml.com/developers/executions NEW
  36. 36. BigML, Inc 36 WhizzML PageNEW https://bigml.com/whizzml
  37. 37. BigML, Inc 37 Documentation Getting Started with WhizzML The BigML Team Version 1.0 MACHINE LEARNING MADE BEAUTIFULLY SIMPLE Copyright © 2016, BigML, Inc. WhizzML Reference Manual The BigML Team Version draft MACHINE LEARNING MADE BEAUTIFULLY SIMPLE Copyright © 2016, BigML, Inc. WhizzML Tutorials The BigML Team Version draft MACHINE LEARNING MADE BEAUTIFULLY SIMPLE Copyright © 2016, BigML, Inc. NEW https://bigml.com/whizzml#documentation
  38. 38. BigML, Inc 38 TrainingNEW
  39. 39. BigML, Inc 39 https://bigml.com/events FREE TrainingNEW
  40. 40. BigML, Inc 40 Spring 2016 Release https://bigml.com/releases NEW
  41. 41. BigML, Inc 41 Conclusion •Automation is critical to fulfilling the promise of ML •WhizzML can create workflows that: •Automate repetitive tasks. •Automate model tuning and feature selection. •Combine ML models into more powerful algorithms. •Create shareable and re-usable executions.
  42. 42. Questions? twitter: @whizzml mail: info@bigml.com

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