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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.

Duration: 1.0 day

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Objectives

  • Access data by using Uniform Resource Identifiers (URIs).
  • Connect to cloud data sources with datastores.
  • Use data asset to access specific files or folders.
  • Choose the appropriate compute target.
  • Work with compute instances and clusters.
  • Manage installed packages with environments.
  • Understand environments in Azure Machine Learning.
  • Explore and use curated environments.
  • Create and use custom environments.
  • Convert a notebook to a script.
  • Test scripts in a terminal.
  • Run a script as a command job.
  • Use parameters in a command job.
  • Use MLflow when you run a script as a job.
  • Review metrics, parameters, artifacts, and models from a run.
  • Log models with MLflow.
  • Understand the MLmodel format.
  • Register an MLflow model in Azure Machine Learning.
  • 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.

Content

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.

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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.

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3. Work with environments in Azure Machine Learning

Learn how to use environments in Azure Machine Learning to run scripts on any compute target.

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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.

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5. Track model training with MLflow in jobs

Learn how to track model training with MLflow in jobs when running scripts.

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6. Register an MLflow model in Azure Machine Learning

Learn how to log and register an MLflow model in Azure Machine Learning.

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7. Deploy a model to a managed online endpoint

Learn how to deploy models to a managed online endpoint for real-time inferencing.

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Audience

N/A

Prerequisites

N/A

Certification

product-certification

Course Benefits

product-benefits
  • Career growth
  • Broad Career opportunities
  • Worldwide recognition from leaders
  • Up-to Date technical skills
  • Popular Certification Badges

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