Automated Machine Learning (AutoML) is a configurable wizard that lets you automate the model development part. It lets you focus on the business problem at hand. You don't need to spend days on tuning hyperparameters manually. This can get you up to speed to see your work in action.
Colleagues who can't code can leverage automl tools, fail fast, and ultimately succeed. Here are some advantages:
Frees you from the setting up the development environment
You can compare the performance of many different Machine Learning algorithms at once. Also, you don't need to install different packages or fight with different interfaces, or data frame formats.
Automatic preprocess data
The automl wizard can automatically impute missing values. It can apply pre-processing activities like dealing with outliers, normalizing the data, etc. AutoML can do feature engineering which reduces the time of data preparation.
Automatic Feature Selection
Some features in a dataset can contain highly correlated features. Automl can automatically select the non-correlated features and the features which truly contribute to predictions from the model. Feature importance can help in explaining why a model does a prediction in a particular way.
Automate and Fast Track Model Development Part
AutoML helps you build ML models by automating the selection of hyperparameter values and evaluation metrics on the given data. Automl can automatically determine the machine learning task whether it's a regression, classification, or forecasting task from the input data. You can specify the models that automl can try out for you or blacklist the ones you want to skip.
Test the model and operationalize it
You can quickly test your models with test data and share your model via REST API. Some services like Azure Machine Learning can help you with 1-click model deployment.
AutoML can greatly benefit data citizens, data analysts, and data scientists alike to build and apply ML models to the problems in their domains. The biggest advantage is it saves a lot of development time and you can quickly come to a conclusion about whether it is worth applying ML to solve the business problem with the data you have.