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

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

To earn this Microsoft Applied Skills credential, students 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 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

DP-3007 Training Objectives

• 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
• Managing artifacts using MLflow
• Implement a model for real-time consumption

Course content DP-3007

Module 1: Making data available in Azure Machine Learning

• Description of URIs
• Creating a data warehouse
• Create a data resource
• Exercise: Making data available in Azure Machine Learning

Module 2: Working with Compute Targets in Azure Machine Learning

• Choosing the right process destination
• Creating and using a process instance
• Creating and Using a Processing Cluster
• Exercise: Working with process resources

Module 3 Working with environments in Azure Machine Learning

• Information about the environments
• Exploration and use of maintained environments
• Creating and using custom environments
• Exercise: Working with environments

Module 4: Running a training script as a command job in Azure Machine Learning

• Converting a notebook into a script
• Running a script as a command job
• Using parameters in a command job
• Exercise: Running a training script as a command job

Module 5: Tracking Model Training with MLflow in Jobs

• Tracking metrics with MLflow
• Visualizing metrics and evaluating models
• Exercise: Using MLflow to track training jobs

Module 6: Registering an MLFlow Model in Azure Machine Learning

• Model registration with MLflow
• Description of the MLflow model format
• Registering an MLflow model
• Exercise: Model Registration with MLflow

Module 7 Deploying a Model to a Managed Online Endpoint

• Scanning managed online endpoints
• Deploying an MLflow model to a managed online endpoint
• Deploying a model to a managed online endpoint
• Testing managed online endpoints
• Exercise: Deploying an MLflow model to an online endpoint

Prerequisites

Familiarity with Azure services and experience with Azure Machine Learning and MLflow are 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, students demonstrate the ability to train and manage machine learning models using Azure Machine Learning.

Applied Skills: Explore all credentials in one guide