Automated ML is an emerging field in Machine Learning that helps data scientists to improve their productivity while also helping developers with little data science knowledge to build Machine Learning models and solutions without understanding the complexity of training algorithm selection, configuration, and hyperparameter tuning. With Azure Automated Machine Learning’s capabilities, given a dataset and a few configuration parameters such as the ML problem you want to solve, you will get a trained high-quality machine learning model that you can use for making predictions. In this session, you will learn how to use Automated ML (with the SDK and the UI) while identifying the best approaches and tools to use depending on your scenario.
Cesar De la Torre works as Principal PM for Microsoft Corporation at the Azure Machine Learning team with a focus on Azure Automated ML and MLOps. Previously he worked for the ML.NET (Machine Learning .NET) and .NET product teams in Microsoft. His background, in addition to machine learning and data science (Scikit-Learn, Azure ML, ML.NET) is also centered on software architecture, distributed applications, microservices architecture, Docker containers, orchestrators such as Kubernetes and DevOps.