Data Scientist

"Deploy and Consume Models with Azure Machine Learning" provides learning materials on how to take machine learning models and make them usable in real-world applications using Microsoft's Azure cloud platform. It focuses on the transition of a developed model from its training phase to a deployed and actively used state. You will learn about the essential steps involved in deploying machine learning models. This includes understanding the Azure Machine Learning service and how it facilitates the deployment process. The key learning objective is to grasp how to take a trained model and make it available for making predictions or inferences on new data. This involves configuring the model for deployment, setting up endpoints to access the model, and understanding how to consume the model's predictions within an application or system. This course will equip someone with the knowledge of how to move a machine learning model from a theoretical or experimental phase to a practical, operational one within the Azure cloud environment.

"Explore and Configure the Azure Machine Learning Workspace" is designed to guide individuals through the initial setup and configuration of an Azure Machine Learning Workspace. It serves as an introductory resource for understanding and establishing a foundational environment for machine learning projects within the Azure cloud platform.

The content focuses on providing the essential knowledge for creating and managing an Azure Machine Learning Workspace. You'll learn about the purpose of the workspace as a centralized hub for machine learning activities. This includes understanding the key configuration options and settings necessary to tailor the workspace to your specific project requirements. The material covers the core components and resources that are integral to the workspace, as well as initial considerations for security and access management. Ultimately, this course aims to prepare you to effectively use the Azure Machine Learning environment by giving you the ability to create and manage your own workspace, which is the starting point for building, training, and deploying machine learning models.