Train and Deploy a Machine Learning Model with Azure Machine Learning (DP-3007)
Course Outline
Train and Deploy a Machine Learning Model with Azure Machine Learning (DP-3007) Benefits
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Upon successful completion of this course, students will master essential skills to:
- Make data available in Azure Machine Learning.
- Work with compute targets 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.
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Training Prerequisites
To maximize the benefits of this course, participants should have familiarity with the data science process. While the course doesn't delve deeply into data science concepts, a basic understanding is recommended. Additionally, familiarity with Python is essential, as the course focuses on utilizing the Python SDK for interacting with Azure Machine Learning.
Azure Machine Learning DP-3007 training course Outline
Module 1: 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
Module 2: 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
Module 3: Work with Environments in Azure Machine Learning
- Introduction
- Understand environments
- Explore and use curated environments
- Create and use custom environments
Exercise: Work with environments
Module 4: 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
Module 5: Track Model Training with MLflow in Jobs
- Introduction
- Track metrics with MLflow
- View metrics and evaluate models
Exercise: Use MLflow to track training jobs
Module 6: 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
Module 7: 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
- choosing a selection results in a full page refresh