Train and deploy a machine learning model with Azure Machine Learning
- Offered byMicrosoft
Train and deploy a machine learning model with Azure Machine Learning at Microsoft Overview
Duration | 5 hours |
Total fee | Free |
Mode of learning | Online |
Schedule type | Self paced |
Difficulty level | Beginner |
Official Website | Explore Free Course  |
Credential | Certificate |
Train and deploy a machine learning model with Azure Machine Learning at Microsoft Highlights
- Earn a certificate of completion
Train and deploy a machine learning model with Azure Machine Learning at Microsoft Course details
- 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
- 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.
Train and deploy a machine learning model with Azure Machine Learning at Microsoft Curriculum
Make data available in Azure Machine Learning
Introduction
Understand URIs
Create a datastore
Create a data asset
Exercise - Make data available in Azure Machine Learning
Knowledge check
Summary
Work with compute targets in Azure Machine Learning
Introduction
Choose the appropriate compute target
Create and use a compute instance
Create and use a compute cluster
Exercise - Work with compute resources
Knowledge check
Summary
Work with environments in Azure Machine Learning
Introduction
Understand environments
Explore and use curated environments
Create and use custom environments
Exercise - Work with environments
Knowledge check
Summary
Run a training script as a command job in Azure Machine Learning
Introduction
Convert a notebook to a script
Run a script as a command job
Use parameters in a command job
Exercise - Run a training script as a command job
Knowledge check
Summary
Track model training with MLflow in jobs
Introduction
Track metrics with MLflow
View metrics and evaluate models
Exercise - Use MLflow to track training jobs
Knowledge check
Summary
Register an MLflow model in Azure Machine Learning
Introduction
Log models with MLflow
Understand the MLflow model format
Register an MLflow model
Exercise - Log and register models with MLflow
Knowledge check
Summary
Deploy a model to a managed online endpoint
Introduction
Explore managed online endpoints
Deploy your MLflow model to a managed online endpoint
Deploy a model to a managed online endpoint
Test managed online endpoints
Exercise - Deploy an MLflow model to an online endpoint
Knowledge check
Summary
Train and deploy a machine learning model with Azure Machine Learning at Microsoft Entry Requirements
- None
- Not mentioned
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