This module explains the basic infrastructure components of Microsoft Azure. You'll learn about the physical infrastructure, how resources are managed, and have a chance to create an Azure resource.

Source control is a critical tool used by developers and devops professionals to manage and maintain code. Describe the purpose and use of source control is an introduction to source control. It starts out explaining source control as a concept, then delves into one of the most popular source control platforms around, and finishes with guiding you through creating your first source controlled file and handling updates.

Select the most appropriate Windows Server administration tool for a given situation and learn how to use that tool.

This module helps you plan a container build strategy, explains containers and their structure, introduces Docker, microservices, Azure Container Registry, and related services.

Learn how to design a data ingestion solution for training data used in machine learning projects.

Identify strategies for managing relationships between data entities, such as customers and sales data. Improve data-model performance and scaling by pre-aggregating and denormalizing your data. Use change-feed to maintain your data's referential integrity.

This course explores the key aspects and solutions related to designing a data protection strategy for your organization using Azure services.

A well-designed application health model gives you contextualized observability by using metrics and thresholds, which you consider important for your mission-critical workload.

Learn how to design a model training solution for machine learning projects.

Learn about machine learning operations or MLOps to bring a model from development to production. Identify options for monitoring and retraining when preparing a model for production.

There are many options on Azure to train and consume machine learning models. Which service best fits your scenario can depend on a myriad of factors. Learn how to identify important requirements and when to use which service when you want to use machine learning models.

Learn how to design a model deployment solution and how the requirements of the deployed model can affect the way you train a model.