Automate Machine Learning Pipeline Using MLBox
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How to automate Machine Learning pipeline ?
How to automate Machine Learning pipeline ?Axel de Romblay Speaker @DataHack Summit 2017, the India's largest conference on Artificial Intelligence & Machine Learning
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Speaker Bio:
Erin LeDell is the Chief Machine Learning Scientist at H2O.ai, the company that produces the open source machine learning platform, H2O. Erin received her Ph.D. in Biostatistics with a Designated Emphasis in Computational Science and Engineering from UC Berkeley. Before joining H2O.ai, she was the Principal Data Scientist at Wise.io (acquired by GE in 2016) and Marvin Mobile Security (acquired by Veracode in 2012) and the founder of DataScientific, Inc.
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Enjoy the video: https://youtu.be/42Oo8TOl85I.
Learn more about H2O.ai: https://www.h2o.ai/.
Follow @h2oai: https://twitter.com/h2oai.
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Abstract:
In recent years, the demand for machine learning experts has outpaced the supply, despite the surge of people entering the field. To address this gap, there have been big strides in the development of user-friendly machine learning software that can be used by non-experts. Although H2O has made it easier for practitioners to train and deploy machine learning models at scale, there is still a fair bit of knowledge and background in data science that is required to produce high-performing machine learning models. Deep Neural Networks in particular, are notoriously difficult for a non-expert to tune properly. In this presentation, we provide an overview of the field of "Automatic Machine Learning" and introduce the new AutoML functionality in H2O. H2O's AutoML provides an easy-to-use interface which automates the process of training a large, comprehensive selection of candidate models and a stacked ensemble model which, in most cases, will be the top performing model in the AutoML Leaderboard. H2O AutoML is available in all the H2O interfaces including the h2o R package, Python module and the Flow web GUI. We will also provide simple code examples to get you started using AutoML.
Erin's Bio:
Erin is a Statistician and Machine Learning Scientist at H2O.ai. She is the main author of H2O Ensemble. Before joining H2O, she was the Principal Data Scientist at Wise.io and Marvin Mobile Security (acquired by Veracode in 2012) and the founder of DataScientific, Inc. Erin received her Ph.D. in Biostatistics with a Designated Emphasis in Computational Science and Engineering from University of California, Berkeley. Her research focuses on ensemble machine learning, learning from imbalanced binary-outcome data, influence curve based variance estimation and statistical computing. She also holds a B.S. and M.A. in Mathematics.
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In recent years, the demand for machine learning experts has outpaced the supply, despite the surge of people entering the field. To address this gap, there have been big strides in the development of user-friendly machine learning software that can be used by non-experts. Although H2O and other tools have made it easier for practitioners to train and deploy machine learning models at scale, there is still a fair bit of knowledge and background in data science that is required to produce high-performing machine learning models. Deep Neural Networks in particular, are notoriously difficult for a non-expert to tune properly.
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But do we need it, when there is already available AI as a service solution out there? Do we need to try hard with artificial neural networks? And if we decide to do so, what tools would be a safe bet?
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how our customers productionize machine learning models
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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
- 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/
- 10. Thank you ! Questions ?
Editor's Notes
- #2: 1min
- #3: 1min
- #4: 2min Data preprocessing and model tuning are both repetitive tasks that take a lot of time… A Data Scientist is expensive !
- #5: 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 !
- #6: 1min Performance = computation time + accuracy
- #7: 2min 90% of machine learning tasks follow this pipeline
- #8: 1min Available on PyPI Github with tutos, examples Docs with articles, kaggle kernels, … Performance : tested on Kaggle ! Features : drifts, embeddings, stacking, leak, feature importances,…
- #9: 2min
- #10: 40min
- #11: 10min THANKS ! Q&A ?