IndySA: Machine Learning Made Easy with AutoML Frameworks

This is the source for my AutoML talk.

Recommended order of discovery:

1. Follow the vscode-setup.md guide.
2. Explore the tpot.ipynb Jupyter notebook in Windows' Anaconda/Jupyter environment.
3. Explore the auto-sklearn.ipynb Jupyter notebook in Windows' Anaconda/Jupyter environment. Observe failure when setting up dependencies.
4. Follow the wsl2-setup.md guide.
5. Explore the auto-sklearn.ipynb Jupyter notebook in Ubuntu's Anaconda/Jupyter environment. Observe success!
6. Explore the ml.net sample.

Observations to take away with you:

1. Different AutoML frameworks have their own take at how to do things, some very opinionated about different aspects of machine learning.
2. Generally, these AutoML frameworks are very quick and easy to get started with.
3. The more data you train on, the better your models are.
4. The more time you train for, the better your models are.
5. With good training data, you can get remarkably good models very quickly.