Whizlabs
Azure ML: Explore & Configure the Machine Learning Workspace
Whizlabs

Azure ML: Explore & Configure the Machine Learning Workspace

Whizlabs Instructor

Instructor: Whizlabs Instructor

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

6 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

6 hours to complete
Flexible schedule
Learn at your own pace

Details to know

Shareable certificate

Add to your LinkedIn profile

Recently updated!

June 2025

Assessments

4 assignments

Taught in English

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This course is part of the Exam Prep DP-100: Microsoft Azure Data Scientist Associate Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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  • Gain a foundational understanding of a subject or tool
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There are 2 modules in this course

This module provides a comprehensive foundation in data science and machine learning, equipping learners with the essential knowledge required for the DP-100 certification. Participants will explore key concepts such as data science processes, machine learning fundamentals, and statistical modeling, ensuring a strong grasp of core principles. The module covers different types of machine learning, common terminologies, and data aspects relevant to model development. Learners will also gain insights into best practices for selecting and managing machine learning solutions, including preprocessing techniques and evaluation methodologies. By the end of this module, participants will develop a structured understanding of data science workflows, enabling them to apply these skills effectively in real-world scenarios and certification preparation

What's included

11 videos2 readings2 assignments

This module provides a comprehensive understanding of compute and data management within Azure Machine Learning, equipping learners with the skills to efficiently configure and optimize ML workflows. Participants will explore key concepts such as creating and managing compute instances, clusters, and attached computes within the Azure ML workspace. The module covers CPU vs. GPU selection for different workloads, datastore and data asset management, and leveraging Uniform Resource Identifiers (URIs) for resource identification. Learners will gain expertise in configuring environments, integrating Synapse Spark pools, and training machine learning models with Azure ML. Additionally, the module includes valuable exam tips to ensure learners are well-prepared for certification. By the end of this module, participants will be equipped with practical knowledge to manage compute and data resources effectively within Azure ML for scalable AI solutions

What's included

12 videos1 reading2 assignments

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