DP-3007 | Train and deploy a machine learning model with Azure Machine Learning
 
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.
 
Course Outline
Module 1: Make data available in Azure Machine Learning
Learn about how to connect to data from the Azure Machine Learning workspace. You're introduced to datastores and data assets.
Introduction
Understand URIs
Create a datastore
Create a data asset
Exercise - Make data available in Azure Machine Learning
Knowledge check
Summary
Module 2: Work with compute targets in Azure Machine Learning
Learn how to work with compute targets in Azure Machine Learning. Compute targets allow you to run your machine learning workloads. Explore how and when you can use a compute instance or compute cluster.
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
Module 3: Work with environments in Azure Machine Learning
Learn how to use environments in Azure Machine Learning to run scripts on any compute target.
Introduction
Understand environments
Explore and use curated environments
Create and use custom environments
Exercise - Work with environments
Knowledge check
Summary
Module 4: Run a training script as a command job in Azure Machine Learning
Learn how to convert your code to a script and run it 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
Module 5: Track model training with MLflow in jobs
Learn how to track model training with MLflow in jobs when running scripts.
Introduction
Track metrics with MLflow
View metrics and evaluate models
Exercise - Use MLflow to track training jobs
Knowledge check
Summary
Module 6: Register an MLflow model in Azure Machine Learning
Learn how to log and 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
Module 7: Deploy a model to a managed online endpoint
Learn how to deploy models to a managed online endpoint for real-time inferencing.
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
 
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