What is automated machine learning?
Automated machine learning is the process of taking training data with a defined target feature, and iterating through combinations of algorithms and feature selections to automatically select the best model for your data based on the training scores. The traditional machine learning model development process is highly resource-intensive, and requires significant domain knowledge and time investment to run and compare the results of dozens of models. Automated machine learning simplifies this process by generating models tuned from the goals and constraints you defined for your experiment, such as the time for the experiment to run or which models to blacklist.
How it works
You configure the type of machine learning problem you are trying to solve. Categories of supervised learning are supported:
While automated machine learning is generally available, the forecasting feature is still in public preview.
See the list of models Azure Machine Learning can try when training.
You specify the source and format for the training data. The data must be labeled, and can be stored on your development environment or in Azure Blob Storage. If the data is stored on your development environment, it must be in the same directory as your training scripts. This directory is copied to the compute target you select for training.
In your training script, the data can be read into Numpy arrays or a Pandas dataframe.
You can configure split options for selecting training and validation data, or you can specify separate training and validation data sets.
Configure the compute target that is used to train the model.
Configure the automated machine learning configuration. This controls the parameters used as Azure Machine Learning iterates over different models, hyperparameter settings, and what metrics to look at when determining the best model
Submit a training run.
During training, the Azure Machine Learning service creates a number of pipelines that try different algorithms and parameters. It will stop once it hits the iteration limit you provide, or when it reaches the target value for the metric you specify.
You can inspect the logged run information, which contains metrics gathered during the run. The training run also produces a Python serialized object (
.pkl file) that contains the model and data preprocessing.
A common pitfall of automated machine learning is an inability to see the end-to-end process. Azure Machine Learning allows you to view detailed information about the models to increase transparency into what's running on the back-end. Some models, like linear regression, are considered to be fairly straightforward and therefore easy to understand. But as we add more features and use more complicated machine learning models, understanding them gets more and more difficult. There are two key aspects to transparency in machine learning:
- Awareness of the machine learning pipeline and all the steps involved including data preprocessing/featurization, and hyperparameter values.
- Understanding the relationship between input variables (also known as “features”) and model output. Knowing both the magnitude and direction of the impact of each feature on the predicted value helps better understand and explain the model. This is known as feature importance.
You can enable global feature importance on-demand post training for the pipeline of your choice, or enable it for all pipelines as part of automated ML training. This is a preview feature and we will continue to invest in providing richer information to help you better understand your ML models.
Follow this sample notebook to experiment with model explanations in Azure Machine Learning.
See examples and learn how to build models using Automated Machine Learning:
We'd love to hear your thoughts. Choose the type you'd like to provide:
Our feedback system is built on GitHub Issues. Read more on our blog.