A repository of exercises to support the training.
In this exercise, you'll harness the power of automated machine learning (AutoML) in Azure Machine Learning to streamline the process of training and evaluating machine learning models. Through a series of hands-on tasks, you'll leverage AutoML to efficiently discover the optimal model architecture and hyperparameters for your dataset. Subsequently, you'll deploy and test the trained model to validate its performance in real-world scenarios.
To begin your exploration of Azure Machine Learning, we recommend diving into the module provided by Microsoft Learning:
Azure Machine Learning's automated machine learning feature simplifies and accelerates the model development process by automating various tasks such as feature engineering, algorithm selection, and hyperparameter tuning. By harnessing the capabilities of AutoML, data scientists and developers can focus on higher-level tasks such as problem formulation and model interpretation, while Azure Machine Learning handles the intricacies of model training and optimization.
After completing this exercise, you'll have a solid understanding of how to leverage automated machine learning in Azure Machine Learning to accelerate model development and deployment processes. Stay tuned for upcoming modules where we'll explore advanced topics in machine learning and AI on the Azure platform.
Happy experimenting!