Train and deploy a machine learning model with Azure Machine Learning

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

Course Length: 1 day
Delivery Methods: Multiple delivery options
Course Overview

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 Benefits
  • Efficiently manage and access data using Azure datastores and data assets.
  • Select and configure the most suitable compute options for your machine learning tasks.
  • Run your machine learning models in highly configurable and reproducible environments.
  • Easily convert notebooks to scripts and execute scalable command jobs in Azure.
  • Track and optimize machine learning experiments with powerful MLflow integration.
  • Seamlessly register and manage ML models within the Azure ecosystem.
  • Rapidly deploy and test machine learning models on scalable, managed online endpoints.
Available Delivery Methods
Public Class
Public expert-led online training from the convenience of your home, office or anywhere with an internet connection. Guaranteed to run .
Private Class
Private classes are delivered for groups at your offices or a location of your choice.
Course Outline
  1. Make data available in Azure Machine Learning
    1. Understand URIs
    2. Create a datastore
    3. Create a data asset
  2. Work with compute targets in Azure Machine Learning
    1. Choose the appropriate compute target
    2. Create and use a compute instance
    3. Create and use a compute cluster
  3. Work with environments in Azure Machine Learning
    1. Understand environments
    2. Explore and use curated environments
    3. Create and use custom environments
  4. Run a training script as a command job in Azure Machine Learning
    1. Convert a notebook to a script
    2. Run a script as a command job
    3. Use parameters in a command job
  5. Track model training with MLflow in jobs
    1. Track metrics with MLflow
    2. View metrics and evaluate models
  6. Register an MLflow model in Azure Machine Learning
    1. Log models with MLflow
    2. Understand the MLflow model format
    3. Register an MLflow model
  7. Deploy a model to a managed online endpoint
    1. Explore managed online endpoints
    2. Deploy your MLflow model to a managed online endpoint
    3. Deploy a model to a managed online endpoint
    4. Test managed online endpoints
Class Materials

Each student will receive a comprehensive set of materials, including course notes and all the class examples.

Live Public Class
$681.10 / student
Please select a class.
Live Private Class
  • Private Class for your Team
  • Live training
  • Online or On-location
  • Customizable
  • Expert Instructors
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