Microsoft Applied Skills: Train and deploy a machine learning model with Azure Machine Learning (DP-3007)

On this Microsoft Applied Skills - Train and deploy a machine learning model with Azure Machine Learning course, also known as DP-3007, you’ll demonstrate your ability to train and deploy machine learning models with Azure Machine Learning.

In just 1 day, you'll explore how to set up your Azure Machine Learning workspace, after which you train and deploy a machine learning model. To train a machine learning model with Azure Machine Learning, you need to make data available and configure the necessary compute. After training your model and tracking model metrics with MLflow, you can decide to deploy your model to an online endpoint for real-time predictions.

You will:

• Set up a development environment in Azure Machine Learning
• Prepare data for model training
• Create and configure a model training script as a command job
• Manage artifacts by using MLflow
• Deploy a model for real-time consumption

At the end of this course, you’ll have the option to complete the Microsoft Applied Skills assessment lab, and earn your Microsoft Applied Skills - Train and deploy a machine learning model with Azure Machine Learning credential.

Audience

This course is ideal for:

• AI Engineers

The DP-3007 course will teach you to:

• Make data available in Azure Machine Learning
• Work with compute targets in Azure Machine Learning
• Work with environments in Azure Machine Learning
• Run a training script as a command job in Azure Machine Learning
• Track model training with MLflow in jobs
• Register an MLflow model in Azure Machine Learning
• Deploy a model to a managed online endpoint

Before attending this accelerated course, you should:

• Be familiar with Azure services.
• Have experience with Azure Machine Learning and MLflow.
• Have experience performing tasks related to machine learning by using Python.