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Azure ML: Deploying, Managing, and Experimenting with Models
  • Level Professional
  • Duration 7 hours
  • Course by Whizlabs
  • Offered by

About

This course is designed to provide a comprehensive foundation in Azure Machine Learning, equipping learners with essential skills for managing ML workflows within the Azure ML workspace. Participants will begin by understanding core workspace fundamentals, including environment setup, resource management, and key components for ML experimentation. The course progresses to advanced concepts such as optimizing compute resources, managing datasets effectively, and configuring high-performance ML pipelines.
Learners will gain expertise in scaling ML workloads, fine-tuning data storage strategies, and applying best practices for secure and efficient model deployment. Additionally, the course covers advanced data and compute management techniques to enhance ML operations (MLOps) and ensure seamless integration with Azure services.
This course is structured into multiple modules, each featuring lessons and video lectures that provide theoretical insights and hands-on practice. Participants will engage with approximately 3:00–4:00 hours of instructional content, ensuring both conceptual understanding and practical application. To reinforce learning, graded and ungraded assignments are included within each module to test the ability of learners in real-world scenarios.
Module 1: Experiment with Azure Machine Learning
Module 2: Deploying, Consuming, Managing, and Evaluating Models with Azure Machine Learning

By the end of this course, a learner will be able to
Explore the process of registering, logging, and deploying MLflow models
Understand and implement Responsible AI practices
Understand the fundamentals of AutoML in Azure
Learn about different machine learning algorithms and tasks
Master how to interpret AutoML job results, ensuring success and optimizing model performance.

Skills you learn

Modules

Azure AutoML: From Data Prep to Model Evaluation

    • 11 Videos
    • 3 Readings
    • 2 Assignment
Show info about module content

2 Assignment

  • Azure AutoML: From Data Prep to Model Evaluation - Practice Assignment
  • Experiment with Azure Machine Learning - Graded Assignment

11 Videos

  • Introducing AutoML
  • Preprocess data and configure featurization
  • Run an Automated Machine Learning experiment
  • Machine Learning Algorithms
  • Different Types of Machine Learning Tasks
  • Evaluate and compare models
  • Exploring Preprocessing Steps in Azure Machine Learning
  • Configure MLflow for model tracking in notebooks
  • Setting and Running an AutoML job
  • Understanding an AutoML job success
  • Exam Tips

3 Readings

  • Welcome to the Course
  • Experiment with Azure Machine Learning - Overview
  • Meet & Greet

Manage and evaluate models with Azure ML

    • 13 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Manage and evaluate models with Azure ML - Practice Assignment

13 Videos

  • Introduction To Exploring how to Register and Deploy Machine Learning Models Using MLflow
  • Logging machine learning models using MLflow
  • Use Autologging to log a model
  • Understand the MLflow model format
  • Configuring the Signature for MLflow Models in Azure Machine Learning
  • Registering an MLflow Model in Azure Machine Learning
  • Understand Responsible AI
  • Evaluating the Responsible AI Dashboard in Azure Machine Learning
  • Exploring Error Analysis in the Responsible AI Dashboard
  • Explore Explanations
  • Explore Counterfactuals and Causal Analysis
  • Registering a Model in Azure Machine Learning
  • Exam Tips

1 Readings

  • Deploying, Consuming, Managing, and Evaluating Models with Azure Machine Learning - Overview

Deploy and consume models with Azure ML

    • 5 Videos
    • 2 Assignment
Show info about module content

2 Assignment

  • Deploy and consume models with Azure ML - Practice Assignment
  • Deploying, Consuming, Managing, and Evaluating Models with Azure Machine Learning - Graded Assignment

5 Videos

  • Deploy a model to a managed online endpoint
  • Managed Online Endpoint
  • Deploy MLflow Model to a Managed Online Endpoin
  • Blue-Green Deployment
  • Exam Tips
Whizlabs Instructor
Instructor

Whizlabs Instructor