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. Throughout this one-day course, you explore how to set up your Azure Machine Learning workspace, after which you train and deploy a machine learning model.

Microsoft Applied Skills

Microsoft Applied Skills are scenario-based credentials that provide learners with validation of targeted skills. These credentials are an efficient and trusted way to identify and deepen proficiency in scenario-based skillsets. The interactive training and validation enable learners to demonstrate proficiency by completing real-world tasks.

Applied Skills can help students prepare for the workforce by providing them with real-world problem-solving experience and validation of their skills.

This course is for:

• AI Engineers
• Data Engineers
• Developers
• Data Scientists

There are no pre-requisites for this course.

After completing this course, students will be able to:

• Access data by using Uniform Resource Identifiers (URIs)
• Connect to cloud data sources with datastores
• Use data asset to access specific files or folders
• Choose the appropriate compute target
• Create and use a compute instance
• Create and use a compute cluster
• Understand environments in Azure Machine Learning
• Explore and use curated environments
• Create and use custom environments
• Convert a notebook to a script
• Test scripts in a terminal
• Run a script as a command job
• Use parameters in a command job
• Use MLflow when you run a script as a job
• Review metrics, parameters, artifacts, and models from a run
• Log models with MLflow
• Understand the MLmodel format
• Register an MLflow model in Azure Machine Learning
• 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

• 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