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Train and manage a machine learning model with Azure Machine Learning (DP-3007)

Explore 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 manage a machine learning model.

GK# 834031 Vendor# DP-3007
Vendor Credits:
  • Global Knowledge Delivered Course
  • Training Exclusives
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Who Should Attend?

  • Machine Learning Professionals
  • Machine Learning Engineers

What You'll Learn
  • 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

Course Outline

Module 1: 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.

Module 2 : Work with compute targets in Azure Machine Learning

  • Choose the appropriate compute target.
  • Work with compute instances and clusters.
  • Manage installed packages with environments.

Module 3: Work with environments in Azure Machine Learning

  • Understand environments in Azure Machine Learning.
  • Explore and use curated environments.
  • Create and use custom environments.

Module 4: 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.

Module 5: 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.

Module 6: Register an MLflow model in Azure Machine Learning

  • Log models with MLflow.
  • Understand the MLmodel format.
  • Register an MLflow model in Azure Machine Learning.

Module 7: 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.
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Related Certifications

Microsoft Applied Skills: Train and manage a machine learning model with Azure Machine Learning