Microsoft Applied Skills
Targeted validation for real-world scenarios. Skill up for in-demand technical scenarios to demonstrate proficiency in specific, scenario-based skill sets so you can make a bigger impact on every project, at your organization, and in your career.
Overview
Validate your technical skills and open doors to new possibilities of advancement with Microsoft Applied Skills.
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 learning path, you explore how to set up your Azure Machine Learning workspace, after which you train and deploy a machine learning model.
Skills Covered
- 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
Prerequisites
There are no prerequisites required to attend this course.
Target Audience
- AI Engineer
- Data Engineer
- Developer
- Data Scientist
Module 1: Make data available in Azure Machine Learning
Learn about how to connect to data from the Azure Machine Learning workspace. You’re introduced to datastores and data assets.
Learning objectives
In this module, you learn how 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.
Module 2: Work with compute targets in Azure Machine Learning
Learn how to work with compute targets in Azure Machine Learning. Compute targets allow you to run your machine learning workloads. Explore how and when you can use a compute instance or compute cluster.
Learning objectives
In this module, you’ll learn how to:
- Choose the appropriate compute target.
- Work with compute instances and clusters.
- Manage installed packages with environments.
Module 3: Work with environments in Azure Machine Learning
Learn how to use environments in Azure Machine Learning to run scripts on any compute target.
Learning objectives
In this module, you’ll learn how to:
- Understand environments in Azure Machine Learning.
- Explore and use curated environments.
- Create and use custom environments.
Module 4: Run a training script as a command job in Azure Machine Learning
Learn how to convert your code to a script and run it as a command job in Azure Machine Learning.
Learning objectives
In this module, you’ll learn how to:
- Convert a notebook to a script.
- Test scripts in a terminal.
- Run a script as a command job.
- Use parameters in a command job.
Module 5: Track model training with MLflow in jobs
Learn how to track model training with MLflow in jobs when running scripts.
Learning objectives
In this module, you learn how to:
- Use MLflow when you run a script as a job.
- Review metrics, parameters, artifacts, and models from a run.
Module 6: Register an MLflow model in Azure Machine Learning
Learn how to log and register an MLflow model in Azure Machine Learning.
Learning objectives
In this module, you’ll learn how to:
- Log models with MLflow.
- Understand the MLmodel format.
- Register an MLflow model in Azure Machine Learning.
Module 7: Deploy a model to a managed online endpoint
Learn how to deploy models to a managed online endpoint for real-time inferencing.
Learning objectives
In this module, you’ll learn how to:
- 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.
Dates & Locations
Exam & Certification
To earn this Microsoft Applied Skills credential, you need to demonstrate your ability to train and manage machine learning models with Azure Machine Learning.
As a candidate for this credential, 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.
Training & Certification Guide
Frequently Asked Questions
Speak to a Training Consultant
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