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Automate Machine Learning Pipeline Using MLBox

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Axel de Romblay
Axel de Romblay

Speaker @DataHack Summit 2017, the India's largest conference on Artificial Intelligence & Machine Learning

Science
1 of 10
Hack Session By Axel de Romblay AUTOMATED MACHINE LEARNING
• Introduction on Auto-ML • MLBox : a powerful Auto-ML python package • Hack session on a dataset AUTOMATED MACHINE LEARNING
Data ScientistData Computation means Data pre-processing Model tuning Machine Learning Almost an automated process…
Auto Machine Learning A fully automated process Data Computation meansRobot • Supervised tasks - classification - regression • Structured data - csv files - json files - … • Unsupervised tasks - outlier detection - clustering - … • Unstructured data - images - texts - …
What is auto-ML ? We want to automate… …the maximum number of steps in a ML pipeline… …with minimum human intervention… …while conserving a high performance !
Data cleaning (duplicates, ids, correlations, leaks, … ) Data encoding (NA, dates, text, categorical features, … ) STEP 2 : Preprocessing STEP 1 : Reading / merging STEP 3 : Optimisation Feature selection Feature engineering Model selection Prediction Model interpretation STEP 4 : Application Focus on the automation process Diagram of a standard ML pipeline
 Quality: functional code : tested on Kaggle  Performance: fully distributed and optimised  AI: dumping and automatic reading of computations  Updates: latest algorithms MLBox: a fully automated python package  Compatibility: Python 2.7-3.6, Linux OS  Quick setup: $ pip install mlbox  User friendly: tutorials, docs, examples…
Hack Session https://www.kaggle.com/c/two-sigma-connect-rental-listing-inquiries Manual kernel : https://www.kaggle.com/sudalairajkumar/xgb-starter-in-python/ Auto kernel : https://www.kaggle.com/axelderomblay/mlbox-a-fully-automated-package/
Thank you ! Questions ?

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Automate Machine Learning Pipeline Using MLBox

  • 1. Hack Session By Axel de Romblay AUTOMATED MACHINE LEARNING
  • 2. • Introduction on Auto-ML • MLBox : a powerful Auto-ML python package • Hack session on a dataset AUTOMATED MACHINE LEARNING
  • 3. Data ScientistData Computation means Data pre-processing Model tuning Machine Learning Almost an automated process…
  • 4. Auto Machine Learning A fully automated process Data Computation meansRobot • Supervised tasks - classification - regression • Structured data - csv files - json files - … • Unsupervised tasks - outlier detection - clustering - … • Unstructured data - images - texts - …
  • 5. What is auto-ML ? We want to automate… …the maximum number of steps in a ML pipeline… …with minimum human intervention… …while conserving a high performance !
  • 6. Data cleaning (duplicates, ids, correlations, leaks, … ) Data encoding (NA, dates, text, categorical features, … ) STEP 2 : Preprocessing STEP 1 : Reading / merging STEP 3 : Optimisation Feature selection Feature engineering Model selection Prediction Model interpretation STEP 4 : Application Focus on the automation process Diagram of a standard ML pipeline
  • 7.
  • 8.  Quality: functional code : tested on Kaggle  Performance: fully distributed and optimised  AI: dumping and automatic reading of computations  Updates: latest algorithms MLBox: a fully automated python package  Compatibility: Python 2.7-3.6, Linux OS  Quick setup: $ pip install mlbox  User friendly: tutorials, docs, examples…
  • 9. Hack Session https://www.kaggle.com/c/two-sigma-connect-rental-listing-inquiries Manual kernel : https://www.kaggle.com/sudalairajkumar/xgb-starter-in-python/ Auto kernel : https://www.kaggle.com/axelderomblay/mlbox-a-fully-automated-package/

Editor's Notes

  1. 1min
  2. 1min
  3. 2min Data preprocessing and model tuning are both repetitive tasks that take a lot of time… A Data Scientist is expensive !
  4. 2min So why don’t we replace the DS by a robot ??? We would save time and money ! Let’s see what can be automated !
  5. 1min Performance = computation time + accuracy
  6. 2min 90% of machine learning tasks follow this pipeline
  7. 1min Available on PyPI Github with tutos, examples Docs with articles, kaggle kernels, … Performance : tested on Kaggle ! Features : drifts, embeddings, stacking, leak, feature importances,…
  8. 2min
  9. 40min
  10. 10min THANKS ! Q&A ?