Want to save time on training your machine learning models? Join my session to discover how Azure ML Automated ML empowers data scientists, analysts, and developers to build high-quality machine learning models with minimal effort and time.

This powerful tool automates the tedious and iterative tasks of model development, including data preprocessing, feature engineering, algorithm selection, hyperparameter tuning, and model evaluation. It supports a variety of machine learning tasks such as classification, regression, forecasting, computer vision, and natural language processing.

In this presentation, we will demonstrate how to use Azure ML Automated ML to create and deploy machine learning models for various scenarios, utilizing both the no-code UI and the Python SDK.

We will discuss the benefits and challenges of using Automated ML, along with best practices and tips for achieving optimal results. Finally, we will show how to interpret and explain the models generated by Automated ML using built-in responsible machine learning solutions.

learning objectives for the session:

Understand how Azure ML Automated ML streamlines the machine learning model development process, including data preprocessing, feature engineering, algorithm selection, hyperparameter tuning, and model evaluation.
Learn to create and deploy machine learning models for various scenarios using both the no-code UI and the Python SDK in Azure ML Automated ML.
Explore best practices for achieving optimal results with Automated ML, and how to interpret and explain models using built-in responsible machine learning solutions.