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49 lines
3.1 KiB
Markdown
49 lines
3.1 KiB
Markdown
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# KDD 2022 Hands-on Tutorial - Automated Machine Learning & Tuning with FLAML
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## Session Information
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Date: August 16, 2022
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Time: 9:30 AM ET
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Location: 101
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Duration: 3 hours
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For the most up-to-date information, see the [SIGKDD'22 Program Agenda](https://kdd.org/kdd2022/handsOnTutorial.html)
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## [Tutorial Slides](https://1drv.ms/b/s!Ao3suATqM7n7ioQF8xT8BbRdyIf_Ww?e=qQysIf)
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## What Will You Learn?
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- What FLAML is and how to use it to find accurate ML models with low computational resources for common machine learning tasks
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- How to leverage the flexible and rich customization choices to:
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- Finish the last mile for deployment
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- Create new applications
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- Code examples, demos, and use cases
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- Research & development opportunities
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## Session Agenda
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### Part 1
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- Overview of AutoML and FLAML
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- Task-oriented AutoML with FLAML
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- [Notebook: A classification task with AutoML](https://github.com/microsoft/FLAML/blob/tutorial/notebook/automl_classification.ipynb); [Open In Colab](https://colab.research.google.com/github/microsoft/FLAML/blob/tutorial/notebook/automl_classification.ipynb)
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- [Notebook: A regression task with AuotML using LightGBM as the learner](https://github.com/microsoft/FLAML/blob/tutorial/notebook/automl_lightgbm.ipynb); [Open In Colab](https://colab.research.google.com/github/microsoft/FLAML/blob/tutorial/notebook/automl_lightgbm.ipynb)
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- [ML.NET demo](https://docs.microsoft.com/dotnet/machine-learning/tutorials/predict-prices-with-model-builder)
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- Tune user defined functions with FLAML
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- [Notebook: Basic tuning procedures and advanced tuning options](https://github.com/microsoft/FLAML/blob/tutorial/notebook/tune_demo.ipynb); [Open In Colab](https://colab.research.google.com/github/microsoft/FLAML/blob/tutorial/notebook/tune_demo.ipynb)
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- [Notebook: Tune pytorch](https://github.com/microsoft/FLAML/blob/tutorial/notebook/tune_pytorch.ipynb); [Open In Colab](https://colab.research.google.com/github/microsoft/FLAML/blob/tutorial/notebook/tune_pytorch.ipynb)
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- Q & A
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### Part 2
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- Zero-shot AutoML
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- [Notebook: Zeroshot AutoML](https://github.com/microsoft/FLAML/blob/tutorial/notebook/zeroshot_lightgbm.ipynb); [Open In Colab](https://colab.research.google.com/github/microsoft/FLAML/blob/tutorial/notebook/zeroshot_lightgbm.ipynb)
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- Time series forecasting
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- [Notebook: AutoML for Time Series Forecast tasks](https://github.com/microsoft/FLAML/blob/tutorial/notebook/automl_time_series_forecast.ipynb); [Open In Colab](https://colab.research.google.com/github/microsoft/FLAML/blob/tutorial/notebook/automl_time_series_forecast.ipynb)
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- Natural language processing
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- [Notebook: AutoML for NLP tasks](https://github.com/microsoft/FLAML/blob/tutorial/notebook/automl_nlp.ipynb); [Open In Colab](https://colab.research.google.com/github/microsoft/FLAML/blob/tutorial/notebook/automl_nlp.ipynb)
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- Online AutoML
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- [Notebook: Online AutoML with Vowpal Wabbit](https://github.com/microsoft/FLAML/blob/tutorial/notebook/autovw.ipynb); [Open In Colab](https://colab.research.google.com/github/microsoft/FLAML/blob/tutorial/notebook/autovw.ipynb)
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- Fair AutoML
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- Challenges and open problems
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