In this lab you will setup the Azure Machine Learning service from code and create a classical machine learning model that logs metrics collected during model training

In this lab you will use the capabilities of the Azure Machine Learning service to collect model performance metrics and to capture model version, as well as query the experimentation run history to retrieve captured metrics

In this lab you will deploy a trained model to containers using an Azure Container Instance and and Azure Kubernetes Service using the Azure Machine Learning SDK

In this lab you train deep learning models built with Keras and a Tensorflow backend that utilize GPUs with the Azure Machine Learning service.

In this lab you deploy a trained model container to an IoT Edge device.

Self-paced Lab: Acquire the cloud skills you need, at your own pace.

In this lab, you will create multiple Azure Cosmos DB containers using different partition keys and settings. You will then use the SQL API and .NET SDK to query specific containers using a single partition key or across multiple partition keys.

In this lab, you will populate an Azure Cosmos DB container from an existing set of data using tools built in to Azure. After importing, you will use the Azure portal to view your imported data.

In this lab you are going to use the .NET SDK to issue similar queries.

In this lab, you will author and execute multiple stored procedures within your Azure Cosmos DB instance. You will explore features unique to JavaScript stored procedures such as throwing errors for transaction rollback, logging using the JavaScript console and implementing a continuation model within a bounded execution environment.

In this lab, you will author and execute multiple stored procedures within your Azure Cosmos DB instance. You will explore features unique to JavaScript stored procedures such as throwing errors for transaction rollback, logging using the JavaScript console and implementing a continuation model within a bounded execution environment.

In this lab you will use the Change Feed Processor Library and Azure Functions to implement three use cases for the Azure Cosmos DB Change Feed.

In this lab, you will use the .NET SDK to tune Azure Cosmos DB requests to optimize the performance and cost of your application.

The SQL API supports optimistic concurrency control (OCC) through HTTP entity tags, or ETags. Every SQL API resource has an ETag, and the ETag is set on the server every time an item is updated. In this lab, we will view the ETag property of a resource that is requested using the SDK.

Self-paced Lab: Acquire the cloud skills you need, at your own pace.