This workshop spans over three days of learning based on artifacts like presentations, hands-on labs, and interactive activities. The three days are structured as follows:

Day 1

Day 1 is focused on Data Science and Azure Machine Learning fundamentals. It starts with an architectural overview of Azure Machine Learning followed by a demo of the Azure Machine Learning workspace. Next, it touches on the Data Science process and the organization and inner workings of a Data Science team. Following the Data Science topics, the day covers two of Azure Machine Learning’s core infrastructure aspects: compute resources and security. The day also touches on the critical aspects of responsible Artificial Intelligence and Machine Learning.

Day 2

Day 2 is focused on Azure Machine Learning development. It starts with the topic of no-code development followed by a discussion on Automated ML. Next, it covers the two main approaches used in code-first development: the Azure Machine Learning SDK for Python and the Azure CLI extension for Azure Machine Learning. Both presentations on these topics are doubled by practical exercises in the form of hands-on labs.

Day 3

Day 3 is focused on Azure Machine Learning integration tasks ranging from data integration and data processes to Machine Learning operations (MLOps). The day starts addressing two of the core data tasks in Machine Learning: privacy protection and drift monitoring. Next, the integration scenarios with Azure Synapse Analytics and Azure Databricks are covered. MLOps is also discussed and practiced via a hands-on lab. The day ends with a high-level interactive discussion on how Azure is being used in modern data science projects.