Whizlabs
Azure ML: Designing and Preparing Machine Learning Solutions
Whizlabs

Azure ML: Designing and Preparing Machine Learning Solutions

Whizlabs Instructor

Instructor: Whizlabs Instructor

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

Recommended experience

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

Recommended experience

9 hours to complete
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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Build your subject-matter expertise

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.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
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There are 3 modules in this course

This course provides a comprehensive understanding of data science and machine learning, focusing on essential concepts and their applications. It emphasizes the fundamental principles of data analysis, statistical modeling, and machine learning techniques, fostering a strong foundation for practical implementation. Participants will gain valuable insights into different types of machine learning, real-world use cases, and best practices for selecting appropriate models. The course also covers key ML terminology, data preprocessing, and the statistical foundations necessary for building robust solutions, preparing learners for both theoretical evaluation and hands-on projects.

What's included

12 videos2 readings2 assignments1 discussion prompt

This course provides an in-depth understanding of managing and utilizing datasets within Azure ML workflows using Azure Data Factory and Synapse Analytics. It emphasizes the principles of configuring and managing Azure Machine Learning environments through the CLI and SDK (v2), ensuring seamless integration and automation. Participants will explore techniques for sharing assets across workspaces, optimizing scalability with registries, and designing efficient ML workflows. Additionally, the course delves into monitoring, retraining, and scaling ML models using Apache Spark and MLOps practices, reinforcing best practices for lifecycle management in production environments.

What's included

7 videos1 reading2 assignments

This course provides a deep dive into identifying appropriate data sources, formats, and ingestion strategies for machine learning projects in Azure, ensuring efficient data handling. It emphasizes the principles of selecting the right services and compute options for model training, optimizing performance and scalability. Participants will gain expertise in differentiating between real-time and batch deployment strategies based on consumption needs, enabling informed architectural decisions. Additionally, the course explores MLOps best practices, guiding learners through the design and implementation of scalable workflows and effective Azure ML environment organization, ensuring seamless integration and lifecycle management.

What's included

12 videos1 reading2 assignments

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