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Managing Machine Learning workflows on Treasure Data

Aki Ariga
Aki Ariga

2018/10/17にTECH PLAY SHIBUYAで開催されたPLAZMA TD Tech Talkの発表です。

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Copyright 1995-2018 Arm Limited (or its affiliates). All rights reserved.
Aki Ariga | Software Engineer
Managing Machine
Learning Workflows
on Treasure Data
Copyright 1995-2018 Arm Limited (or its affiliates). All rights reserved.
● Aki Ariga (a.k.a. @chezou)
● Software Engineer at Machine Learning team
● Co-author of 「仕事ではじめる機械学習」
● Founder of kawasaki.rb & MLCT
● Interesting: MLOps, ML deployment/management
Who am I?
Copyright 1995-2018 Arm Limited (or its affiliates). All rights reserved.
Machine Learning on
Treasure Data
Copyright 1995-2018 Arm Limited (or its affiliates). All rights reserved.
Machine Learning capability on Treasure Data
Treasure CDP UI:
GUI based, handy
SQL+workflow: Scalable Integrate with third-party
ML toolkit
Copyright 1995-2018 Arm Limited (or its affiliates). All rights reserved.
Apache Hivemall (incubating)
● Scalable ML library implemented as Hive UDFs
● OSS project under Apache Software Foundation
● TD bundles Hivemall and has 3 developers (creator + 2 core committers)
Easy-to-use
ML in SQL
Scalable
Runs in parallel on
Hadoop ecosystem
Versatile
Efficient, generic
functions
Copyright 1995-2018 Arm Limited (or its affiliates). All rights reserved.
Example SQL for training with supervised learning
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Managing Machine Learning workflows on Treasure Data

  • 1. Copyright 1995-2018 Arm Limited (or its affiliates). All rights reserved. Aki Ariga | Software Engineer Managing Machine Learning Workflows on Treasure Data
  • 2. Copyright 1995-2018 Arm Limited (or its affiliates). All rights reserved. ● Aki Ariga (a.k.a. @chezou) ● Software Engineer at Machine Learning team ● Co-author of 「仕事ではじめる機械学習」 ● Founder of kawasaki.rb & MLCT ● Interesting: MLOps, ML deployment/management Who am I?
  • 3. Copyright 1995-2018 Arm Limited (or its affiliates). All rights reserved. Machine Learning on Treasure Data
  • 4. Copyright 1995-2018 Arm Limited (or its affiliates). All rights reserved. Machine Learning capability on Treasure Data Treasure CDP UI: GUI based, handy SQL+workflow: Scalable Integrate with third-party ML toolkit
  • 5. Copyright 1995-2018 Arm Limited (or its affiliates). All rights reserved. Apache Hivemall (incubating) ● Scalable ML library implemented as Hive UDFs ● OSS project under Apache Software Foundation ● TD bundles Hivemall and has 3 developers (creator + 2 core committers) Easy-to-use ML in SQL Scalable Runs in parallel on Hadoop ecosystem Versatile Efficient, generic functions
  • 6. Copyright 1995-2018 Arm Limited (or its affiliates). All rights reserved. Example SQL for training with supervised learning
  • 7. Copyright 1995-2018 Arm Limited (or its affiliates). All rights reserved. Treasure Workflow (a.k.a. digdag)
  • 8. Copyright 1995-2018 Arm Limited (or its affiliates). All rights reserved. Treasure Workflow for ML ● Easy to productionize your ML workflow for training/prediction ● Easy to get benefit from parallelization +parameter_tuning: for_each>: eta0: [5.0, 1.0, 0.5, 0.1, 0.05, 0.01, 0.001] reg: ['no', 'rda', 'l1', 'l2', 'elasticnet'] _parallel: true _do: +train: td>: queries/train_regressor.sql suffix: _${reg}_${eta0.toString().replace('.', '_')} create_table: regressor${suffix} +evaluate: td>: queries/evaluate_params.sql insert_into: accuracy_test suffix: _${reg}_${eta0.toString().replace('.', '_')}
  • 9. Copyright 1995-2018 Arm Limited (or its affiliates). All rights reserved. No secrets with ML workflows on TD... ● EDA with a Jupyter notebook to investigate data distribution, trend, outliers, etc ● Reuse queries in the training phase with prediction as much as possible ● Write commit message with accuracies for queries and workflows ○ A digdag workflow is a code. Versioning workflow is easy ● Versioning models. A Hivemall model is just a table! ○ Logistic Regression/Linear Regression model weights help to understand feature importance ● Visualize table/query dependencies with existing workflow But I can tell some tips
  • 10. Copyright 1995-2018 Arm Limited (or its affiliates). All rights reserved. Visualize table/query dependency Extract dependencies from CREATE/INSERT TABLE statements
  • 11. Copyright 1995-2018 Arm Limited (or its affiliates). All rights reserved. Machine Learning capability on Treasure Data (revisit) Treasure CDP UI: GUI based, handy SQL + Workflow: Scalable Integrate with third-party ML toolkit
  • 12. Copyright 1995-2018 Arm Limited (or its affiliates). All rights reserved. Pros/Cons Treasure CDP UI Hivemall + Treasure Workflow Integrate third-party tools Pros - Easy to use - Fully integrated with web based GUI Cons - Specific purposes (Predictive scoring, Customer tagging) Pros - Customizable - Scalable with big data - Recommendation - Scheduled train/predict Cons - Different paradigm with Python scripting Pros - Flexibility with familiar frameworks - Model portability Cons - Data transfer time - Need to prepare your own machine
  • 13. Copyright 1995-2018 Arm Limited (or its affiliates). All rights reserved. Pros/Cons with py> operator on TD Treasure CDP UI Hivemall + Treasure Workflow Third-party tools with py> operator on TD (private alpha) Pros - Easy to use - Fully integrated with web based GUI Cons - Specific purposes (Predictive scoring, Customer tagging) Pros - Customizable - Scalable with big data - Recommendation - Scheduled train/predict Cons - Different paradigm with Python scripting Pros - Flexibility with familiar frameworks - Model portability - Scheduled train/predict Cons - Data transfer time - Not scalable - Need to prepare your own machine
  • 14. Copyright 1995-2018 Arm Limited (or its affiliates). All rights reserved. What we can do with py> operator on TD?
  • 15. Copyright 1995-2018 Arm Limited (or its affiliates). All rights reserved. Current architecture Data SQL + Treasure Data Customer environment Heavy data Aggregated dataScheduled execution with Treasure Workflow Ad-hoc query
  • 16. Copyright 1995-2018 Arm Limited (or its affiliates). All rights reserved. Architecture with py> operator Data SQL + Treasure Data Container on TD Heavy data Aggregated data Scheduled execution with Treasure Workflow Model import/export Ad-hoc query based modeling available as well Prediction results Prediction results
  • 17. Copyright 1995-2018 Arm Limited (or its affiliates). All rights reserved. Demo: Time series prediction with Prophet
  • 18. Copyright 1995-2018 Arm Limited (or its affiliates). All rights reserved. py> operator is good for... ● Experimenting on your machine, productionizing on TD ○ Scheduled prediction with customer trained model using Python ML libraries and write back prediction results to TD ○ E.g. Build TF/sklearn model on a customer’s machine and predict on TD ● Exporting models for customer own prediction APIs ○ E.g. Build a model with PyTorch, export ONNX and build own API server ● Updating your model continuously on TD ○ E.g. Train TF model with GPU and predict on TD (not planned yet) ● Data preparation/enrichment with Python script ○ E.g. Complex text analysis
  • 19. Copyright 1995-2018 Arm Limited (or its affiliates). All rights reserved. Batch prediction vs on-the-fly prediction ● TD has a strong capability for batch prediction with Hivemall ○ In batch manner, storing prediction results is the most easiest way ● We don’t have any option for on-the-fly prediction yet ○ Option 1) Export models on S3 and customer will build their own API servers ○ Option 2) Build APIs for each customer’s model on TD (not planned yet)
  • 20. Copyright 1995-2018 Arm Limited (or its affiliates). All rights reserved. Example workflows with py> operator ● Time series prediction for sales with Prophet ○ https://github.com/treasure-data/workflow-examples/pull/117 ● Sentiment classification with TensorFlow ○ https://github.com/treasure-data/workflow-examples/pull/118 ● Feature selection with scikit-learn ○ https://github.com/treasure-data/workflow-examples/pull/116
  • 21. Confidential © Arm 2017Confidential © Arm 2017Confidential © Arm 2017 Thank You! Danke! Merci! 谢谢! ありがとう! Gracias! Kiitos!