Security, Data, and Machine Learning in the Cloud (5cr)

Objectives

• The student understands the fundamental principles of security in cloud environments, including identity and access management, governance, and compliance.
• The student can apply data engineering concepts to design and manage data pipelines for ingestion, storage, processing and analysis in the cloud.
• The student can explain core machine learning concepts and use cloud-based services to build, train, and deploy basic machine learning models.

Content

• Fundamental principles of cloud security, identity and access management, and compliance.
• Strategies for collecting, storing, processing, and analyzing data in the cloud.
• Design and implementation of data pipelines to support analytics and machine learning.
• Core concepts of artificial intelligence, machine learning, and generative AI.
• Practice in building, training, and deploying machine learning models.

Prerequisites

• Basic programming skills.
• Experience with databases and SQL.
• Completed the Introduction to Cloud Services and Architecture, Cloud-Based Software Development and Deployment Processes or a similar course.

Assessment criteria, satisfactory (1-2)

The student knows the basic concepts of cloud security, data engineering, and machine learning, and can describe simple use cases in each area.

Assessment criteria, good (3-4)

The student masters the key principles of cloud security, can design basic data pipelines, and apply foundational machine learning methods using cloud services.

Assessment criteria, excellent (5)

The student demonstrates comprehensive mastery of cloud security, data engineering, and machine learning, can implement integrated solutions that combine these areas, and apply best practices to evaluate and improve cloud-based systems.