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#8697
Train and deploy a machine learning model with Azure Machine Learning (DP-3007)
Course 8697
- Duration: 1 day
- Language: English
- Level: Intermediate
Azure Machine Learning DP-3007 training course Delivery Methods
In-Person
Online
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Azure Machine Learning DP-3007 training course Information
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.
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
- Introduction
- Understand URIs
- Create a datastore
- Create a data asset
Exercise: Make data available 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
- Introduction
- Understand environments
- Explore and use curated environments
- Create and use custom environments
Exercise: Work with environments
- 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
- Introduction
- Track metrics with MLflow
- View metrics and evaluate models
Exercise: Use MLflow to track training jobs
- Introduction
- Log models with MLflow
- Understand the MLflow model format
- Register an MLflow model
Exercise: Log and register models with MLflow
- 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
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