DP-3007: Train and Deploy a Machine Learning Model with Azure Machine Learning Course Outline

Special Note to New Hampshire Residents
This course has not yet been approved by the New Hampshire Department of Education. Please contact us for an update on when the class will be available in New Hampshire.

Overview
The DP-3007: Train and Deploy a Machine Learning Model with Azure Machine Learning course covers the end-to-end process of building, training, and
deploying machine learning models using Azure Machine Learning, including data preparation, model training, and deployment strategies.

Objectives
• 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

Target Audience
• Data Scientist
• AI Engineer

COURSE OUTLINE

Make data available in Azure Machine Learning
• Access data by using Uniform Resource Identifiers URIs
• Connect to cloud data sources with datastores
• Use data asset to access specific files or folders
• Lab Make data available in Azure Machine Learning

Work with compute targets in Azure Machine Learning
• Choose the appropriate compute target
• Work with compute instances and clusters
• Manage installed packages with environments
• Lab Work with compute resources

Work with environments in Azure Machine Learning
• Understand environments in Azure Machine Learning
• Explore and use curated environments
• Create and use custom environments
• Lab Work with environments

Run a training script as a command job in Azure Machine Learning
• Convert a notebook to a script
• Test scripts in a terminal
• Run a script as a command job
• Use parameters in a command job
• Lab Run a training script as a command job

Track model training with MLflow in jobs
• Use MLflow when you run a script as a job
• Review metrics parameters artifacts and models from a run
• Lab Use MLflow to track training jobs

Register an MLflow model in Azure Machine Learning
• Log models with MLflow
• Understand the MLmodel format
• Register an MLflow model in Azure Machine Learning
• Lab Log and register models with MLflow

Deploy a model to a managed online endpoint
• Use managed online endpoints
• Deploy your MLflow model to a managed online endpoint
• Deploy a custom model to a managed online endpoint
• Test online endpoints
• Lab Deploy an MLflow model to an online endpoint
View outline in Word

ADP3A7

Attend hands-on, instructor-led DP-3007: Train and Deploy a Machine Learning Model with Azure Machine Learning training classes at ONLC's more than 300 locations. Not near one of our locations? Attend these same live classes from your home/office PC via our Remote Classroom Instruction (RCI) technology.

For additional training options, check out our list of Courses and select the one that's right for you.