Automatic Machine Learning

Auto is the new black — Google AutoML, Microsoft Automated ML, AutoKeras and auto-sklearn

How they achieve meta-learning and what’s behind them

Motivation: Life is hard

Achieving state-of-the-art performance in a given data set is hard. It usually implies carefully selecting the right data pre-prossessing tasks, picking the right algorithm, model and architecture and pairing it with the right set of parameters. This end-to-end process is usually called Machine Learning Pipeline. There is no rule of thumb in which direction to go and, with more models beings developed all the time, even picking the right model is becoming challenging. Hyper-parameter tuning usually requires walking or sampling over all the possible values and just trying them out. However, there is no any warranty about finding something useful. In this context, automating the selection and tuning of machine learning pipelines has long been one of the goals of the machine learning community. This kind of task are usually referred as meta-learning — learning about learning.

It also seems it’s been in our landscape for the beginning of the times. A funny fairy tale…

Once upon a time, there was a sorcerer who trained models in a framework that does not longer exist, in a programing language that nobody longer codes in. One day, an old man asked him to train a model for a mysterious dataset.