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
