Pentaho World 2017: Automated Machine Learning (AutoML) and Pentaho (Thursday, October 26th, 2017)
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The document discusses how automated machine learning (AutoML) can streamline the model creation and deployment processes using Pentaho, highlighting the challenges data scientists face and the growing demand for user-friendly tools. It emphasizes that while AutoML automates parts of the machine learning workflow, it is designed to support rather than replace data scientists. The presentation covers demonstrations of tools, a structured approach to creating a machine learning model, and next steps for interested participants.
Pentaho World 2017: Automated Machine Learning (AutoML) and Pentaho (Thursday, October 26th, 2017)
- 2. Agenda We will discuss how Automated Machine Learning (AutoML) and Pentaho, together, can help customers save time in the process of creating a model and deploying this model into production. • Business Case for Automated Machine Learning (AutoML) and Pentaho; • High level overview about Automated Machine Learning (AutoML); • Demonstrations (Pentaho + AutoML).
- 4. Business Case for AutoML and Pentaho • Finding the correct machine learning algorithm is not an easy task. • You need to find a balance between the time you would need to spend and the time you can actually spend on the ML problem. • To create a good model you will need to know very well the problem, the variables (instances), prepare the data, feature engineering and test different algorithms. • Some data scientists will also say to add a little bit of MAGIC J. • Adding, of course, in most cases, a lot of computer power.
- 9. What is NOT Automated Machine Learning (AutoML)? • AutoML is not automated data science; • AutoML will not replace Data Scientist; – All the methods of automated machine learning are developed to support data scientists, not to replace them. – AutoML is to free data scientists from the burden of repetitive and time-consuming tasks (e.g., machine learning pipeline design and hyperparameter optimization) so they can better spend their time on tasks that are much more difficult to automate.
- 10. Auto ML Tools • Auto Weka (Open Source) – http://www.cs.ubc.ca/labs/beta/Projects/autoweka/ • H2o.ai AutoML (Open Source) – https://www.h2o.ai/ • TPOT (Open Source) – https://github.com/rhiever/tpot • Auto Sklearn (Open Source) – https://github.com/automl/auto-sklearn – http://automl.github.io/auto-sklearn/stable/ • machineJS (Open Source) – https://github.com/ClimbsRocks/machineJS
- 11. PDI + AutoML
- 14. Use Case: AutoML + Pentaho • Our users have a well defined ML problem and the initial version of the dataset (train and test). • Unfortunately, they haven’t created a ML model yet. • Also, they have no idea how to create it. • And they want us to help them to create it as soon as possible using only Open Source tools.
- 15. The Journey • If you embark in this journey, you can stick in this problem forever… …or you can find quick ways to do it in a specified time. • Customers can then spend enough time later to improve their current Model. • The next steps will be: – Hire a data scientist or a team of data scientists; – Hire a domain expert in that problem.
- 17. Create Your First ML Model 1. Define the problem; 2. Analyze and prepare the data; 3. Select algorithms (start simple); 4. Run and evaluate the algorithms; 5. Improve the results with focused experiments; 6. Finalize results with fine tuning.
- 18. Sample Dataset • More data is better, but more data means more complexity. • More data means more time that you will have to spend in your problem. • Why not create a sample dataset?! – Create 1 to 20 datasets to test your problem and create your models;
- 23. Demonstration
- 24. Demo Agenda What we will cover in the demo: • Data Preparation with PDI; • Model creation using AutoML Tool; • Model Deployment with PDI;
- 26. Summary What we covered today: • Business Case for Automated Machine Learning (AutoML) and Pentaho; • High level overview about Automated Machine Learning (AutoML); • Demonstrations (Pentaho + AutoML).
- 27. Next Steps Want to learn more? • Talk to me during Pentaho World 2017 or send me an e-mail caio.moreno@HitachiVantara.com; • Meet-the-Experts: – https://www.pentahoworld.com/meet-the-experts
- 29. Appendices
- 34. Auto Weka • Auto Weka – provides automatic selection of models and hyperparameters for WEKA. – http://www.cs.ubc.ca/labs/beta/Projects/autoweka/ • Open datasets for Auto Weka – http://www.cs.ubc.ca/labs/beta/Projects/autoweka/datasets/
- 35. Auto Sklearn • Auto Weka inspired the authors of Auto Sklearn; • Auto Sklearn – auto-sklearn is an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator. – https://github.com/automl/auto-sklearn – http://automl.github.io/auto-sklearn/stable/
- 36. Types of ML Problems with (AutoML) • The types of Machine Learning problems that we can solve using Auto Weka and Auto Sklearn are Classification, Regression and Clustering: – Classification and Regression are already supported in Auto-sklearn & Auto-WEKA. – For clustering, you can use as long as you have an objective function to optimize.
- 40. Installing TPOT • Command to install TPOT – $ pip install tpot • Learn more: – http://rhiever.github.io/tpot/installing/
- 41. Installing Auto Sklearn on Ubuntu • Use the documentation below to help you: – http://automl.github.io/auto-sklearn/stable/ • Run this command on ubuntu terminal: – $ conda install gcc swig – $ curl https://raw.githubusercontent.com/automl/auto- sklearn/master/requirements.txt | xargs -n 1 -L 1 pip install – $ sudo apt-get install build-essential swig – $ pip install –U auto-sklearn
- 42. Error Auto Sklearn on Ubuntu • Error reported on June, 14th 2017. Solution sent on the same day. • Check the GitHub link below to find the solution: https://github.com/automl/auto-sklearn/issues/308
- 43. Installing H20.ai • To install H20.ai AutoML visit the websites: – https://blog.h2o.ai/2017/06/automatic-machine-learning/ – https://www.h2o.ai/