Last week I had the pleasure of giving an open AI workshop for the awesome Girls who Code Iasi community. They had approached me earlier this year about such a workshop, and I suggested teaching the audience about Azure Automated Machine Learning, since this is a topic near and dear to me - I think it’s a great way for beginners to start dabbling with machine learning, and for experts to have something up and running in no time.
Apart from this, I wanted to have a tangible goal for this workshop, so I designed it so that the audience would gradually learn to use and understand Automated ML while competing in an open Kaggle competition, the Titanic competition. This may sound a bit familiar 😋.
Long story short, I quite enjoyed the experience, and so did the audience - I guided them through understanding how Automated ML can be used, how it works, and what’s under the hood over the course of 3 hours. I underlined the elegant use of open-source components wherever possible (i.e. scikit—learn pipelines, scalers, algorithms), and taught them how to extend and improve the automatically trained models to suit their scenarios. We discussed automated ML versus hyperparameter optimization, what’s the best approach when evaluating a model, and how we can make sure the models we train can perform well when presented with unknown data.
The cherry on top was using the insights gained from running model explainability over the automatically trained models in order to generate new features that could be used to train better, faster, and stronger models.
In the end we reached top 4% in the public leaderboard (well, technically, it was 3.36% - 538 out of 16,001 teams 🤓). All of this in just three hours, without doing a lot more than just understanding and acting on the insights gained from the automatically generated models. The audience was great as always, energetic, eager to learn, engaged.
All I can say is that it was a lovely experience, one that I hope to be part of again in the future. If you’d like to try out our code and see for yourself, take a look at this GitHub repo.