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Train and Deploy a Machine Learning Model with Azure Machine Learning (DP-3007)
Course 8697
1 DAY COURSE

Price:
Course Outline

Train and Deploy a Machine Learning Model with Azure Machine Learning (DP-3007) Benefits

  • 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

Module 1: Make Data Available in Azure Machine Learning 

  • Introduction 
  • Understand URIs 
  • Create a datastore 
  • Create a data asset 

Exercise: Make data available in Azure Machine Learning 

Module 2: Work with Compute Targets 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 

Module 3: Work with Environments in Azure Machine Learning 

  • Introduction 
  • Understand environments 
  • Explore and use curated environments 
  • Create and use custom environments 

Exercise: Work with environments 

Module 4: Run a Training Script 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 

Module 5: Track Model Training with MLflow in Jobs 

  • Introduction 
  • Track metrics with MLflow 
  • View metrics and evaluate models 

Exercise: Use MLflow to track training jobs 

Module 6: 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 

Module 7: Deploy a Model to a Managed Online Endpoint 

  • 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 

Course Dates
Attendance Method

How will you be attending the class?

Selecting 'Live Virtual' allows you to attend remotely from work or home. You will receive email communication well before the class starts with detailed instructions on how to validate your equipment and connect to the classroom for a quality learning experience.

Additional Details (optional)

Private Team Training

Interested in this course for your team? Please complete and submit the form below and we will contact you to discuss your needs and budget.