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
Who Should Attend
- AI Engineer
- Data Engineer
- Developer
- Data Scientist
Course Curriculum
Course Modules
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.
