Auto ML

How it works

During training, Auto Machine Learning creates a number of pipelines in parallel that try different algorithms and parameters for you. The service iterates through ML algorithms paired with feature selections, where each iteration produces a model with a training score. The higher the score, the better the model is considered to “fit” your data. It will stop once it hits the exit criteria defined in the experiment.

Using Auto Machine Learning, you can design and run your automated ML training experiments with these steps:

1. Identify the ML problem to be solved: classification, forecasting, or regression
2. Choose whether you want to use the Python SDK or the studio web experience.Specify the source and format of the labeled training data: Numpy arrays or Pandas dataframe
3. Configure the compute target for model training, such as your local computer.
4. Configure the automated machine learning parameters that determine how many iterations over different models, hyperparameter settings, advanced preprocessing/featurization, and what metrics to look at when determining the best model.
5. Submit the training run.
6. Review the results

Feature engineering is the process of using domain knowledge of the data to create features that help ML algorithms learn better. In Azure Machine Learning, scaling and normalization techniques are applied to facilitate feature engineering. Collectively, these techniques and feature engineering are referred to as featurization.

For automated machine learning experiments, featurization is applied automatically, but can also be customized based on your data.

In every automated machine learning experiment, your data is automatically scaled or normalized to help algorithms perform well. During model training, one of the following scaling or normalization techniques will be applied to each model. Learn how AutoML helps prevent over-fitting and imbalanced data in your models.

In every automated machine learning experiment, your data is automatically scaled or normalized to help algorithms perform well. During model training, one of the following scaling or normalization techniques will be applied to each model. Learn how AutoML helps prevent over-fitting and imbalanced data in your models.