Course description. DP-3007: Train and deploy a machine learning model with Azure Machine Learning

To earn this Microsoft Applied Skills credential, learners demonstrate the ability to train and manage machine learning models with Azure Machine Learning.

Candidates for this credential should be familiar with Azure services and should have experience with Azure Machine Learning and Mlflow . Candidates should also have experience performing machine learning-related tasks using Python .

Intermediate - Azure, Azure Machine Learning - AI Engineer, Data Engineer, Developer, Data Scientist - Machine Learning

Goals

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

Training Route

Training and managing a machine learning model with Azure Machine Learning

To train a machine learning model with Azure Machine Learning, you need to make data available and set up the necessary process. After you train the model and track model metrics with MLflow, you can decide to deploy the model to an online endpoint for real-time predictions. In this learning path, you will explore how to set up your Azure Machine Learning workspace, after which you will train and manage a machine learning model.

• Make data available in Azure Machine Learning: Learn how to connect to data from your Azure Machine Learning workspace. You are introduced to data stores and data resources.
• Working with environments in Azure Machine Learning: Learn how to use environments in Azure Machine Learning to run scripts against any compute target.
• Running a training script as a command job in Azure Machine Learning: Learn how to convert your code into a script and run it as a command job in Azure Machine Learning.
• Tracking model training with MLflow in jobs: Learn how to track model training with MLflow in jobs by running scripts.
• Registering an MLFlow model in Azure Machine Learning: Learn how to register an MLflow model in Azure Machine Learning.
• Deploy a model to a managed online endpoint - Learn how to deploy models to a managed online endpoint for real-time inference.

Prerequisites

Familiarity with Azure services and experience with Azure Machine Learning and Mlflow is recommended. Additionally, you should have experience performing machine learning-related tasks using Python .

Language

• Course: English / Spanish
• Labs: English / Spanish

Microsoft Applied Skills

This course is part of the Microsoft Applied Skills Credentials.

To earn this Microsoft Applied Skills credential, learners demonstrate the ability to train and manage machine learning models using Azure Machine Learning.

Applied Skills: Explore all credentials in one guide