DP-100 Labs

Microsoft Learn

Note

Microsoft Learn sandboxes are interactive, temporary environments provided by Microsoft Learn that allow users to practice and explore Azure services and other Microsoft technologies without needing their own subscription. These sandboxes are free, time-limited, and isolated, offering a secure space to complete guided learning modules, tutorials, and hands-on exercises in a real-world setup.

Occasionally, they require you to bring your own subscription or run the exercises locally on your computer.

Caution

Be careful of starting multiple sandboxes/visiting multiple sandbox URLs at the same time as Microsoft Learn might suspend your access due to detected suspicious activity.

Explore the developer tools

Make data available in Azure Machine Learning

Work with compute resources

Work with environments

Find the best classification model with Automated Machine Learning

Track model training

Run a training script as a command job

Use MLflow to track training jobs

Run a sweep job

Run a pipeline job

Log and register models with MLflow

Explore the Responsible AI dashboard

Deploy an MLflow model to an online endpoint

Deploy an MLflow model to a batch endpoint

Explore the Azure AI Foundry portal

Explore, deploy, and chat with language models

Create a generative AI chat app

Get started with prompt flow

Create a custom agent that uses your own data

Fine-tune a foundation model

Evaluate the performance of your generative AI app

Apply content filters to prevent the output of harmful content

Microsoft GitHub

Note

Microsoft GitHub labs are series of labs designed to be used in Microsoft Instructor Lead Trainings delivered by Microsoft Certified Trainers where participants also have access to the environment to perform them. This means that for everyone else, it is necessary to bring your own environment to go through them.

Explore the Azure Machine Learning workspace

Explore developer tools for workspace interaction

Make data available in Azure Machine Learning

Work with compute resources in Azure Machine Learning

Work with environments in Azure Machine Learning

Train a model with the Azure Machine Learning Designer

Find the best classification model with Automated Machine Learning

Track model training in notebooks with MLflow

Run a training script as a command job in Azure Machine Learning

Use MLflow to track training jobs

Perform hyperparameter tuning with a sweep job

Run pipelines in Azure Machine Learning

Create and explore the Responsible AI dashboard

Log and register models with MLflow

Deploy a model to a batch endpoint

Deploy a model to a managed online endpoint