autogen/tutorials/flaml-tutorial-kdd-22.md

# KDD 2022 Hands-on Tutorial - Automated Machine Learning & Tuning with FLAML

## Session Information

Date: August 16, 2022
Time: 9:30 AM ET
Location: 101
Duration: 3 hours

For the most up-to-date information, see the [SIGKDD'22 Program Agenda](https://kdd.org/kdd2022/handsOnTutorial.html)

## [Tutorial Slides](https://1drv.ms/b/s!Ao3suATqM7n7ioQF8xT8BbRdyIf_Ww?e=qQysIf)

## What Will You Learn?

- What FLAML is and how to use it to find accurate ML models with low computational resources for common machine learning tasks
- How to leverage the flexible and rich customization choices to:
  - Finish the last mile for deployment
  - Create new applications
- Code examples, demos, and use cases
- Research & development opportunities

## Session Agenda

### Part 1

- Overview of AutoML and FLAML
- Task-oriented AutoML with FLAML
    - [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)
    - [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)
- [ML.NET demo](https://docs.microsoft.com/dotnet/machine-learning/tutorials/predict-prices-with-model-builder)
- Tune user defined functions with FLAML
    - [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)
    - [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)
- Q & A

### Part 2

- Zero-shot AutoML
    - [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)
- Time series forecasting
    - [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)
- Natural language processing
    - [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)
- Online AutoML
    - [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)
- Fair AutoML
- Challenges and open problems