Achieving state-of-the-art performance in a given data set is hard. It usually implies carefully selecting the right data pre-processing tasks, picking the algorithm, model, and architecture and pairing it with the right set of parameters. This end-to-end process is usually called Machine Learning Pipeline. Isn't tough and time-consuming?
Automated ML helps create a high-quality model using intelligent automation and optimization. You launch the automated machine learning process by establishing goals and constraints. Then allow the algorithm selection and hyperparameter tuning to happen for you. The AutoML technique iterates over many combinations of algorithms and hyperparameters until it finds the best model based on your criterion.
In this session, we'll see how to set up the environment, configure an Azure Machine Learning service workspace and explore the results