FM2AZDC10 Data Acquisition and Cloud Services

FM2AZDC10 Data Acquisition and Cloud Services

  • Course description
    • Course Code
      FM2AZDC10
    • Level of Study
      5.2
    • Program of Study
      Applied Artificial Intelligence
    • Credits
      10
    • Study Plan Coordinator
      Bertram Haskins
Teaching Term(s)
2026 Autumn
2027 Spring
About the Course

This course provides students with the knowledge and skills required to acquire, process, and manage data efficiently for artificial intelligence applications. Candidates will explore various data sources, data acquisition techniques, and cloud-based solutions for data storage and model deployment.

Students will gain hands-on experience in building data pipelines, ensuring data quality, and integrating REST-based services for real-time data access. The course also covers the role of big data in modern AI workflows and introduces alternative development tools for AI model creation. By the end of the course, students will be able to design, manage, and deploy scalable data services that support AI-driven applications.

Course Learning Outcomes
Knowledge

The candidate: 

  • has knowledge of theories and models related to big data, distributed storage, parallel processing, and cloud-based infrastructures.
  • understands various data sources and their role in data-driven AI and machine learning workflows.
  • has insight into relevant regulations, standards, agreements and quality requirements related to data ingestion, storage and governance in modern AI workflows.
  • can assess his/her own work in relation to the applicable norms and requirements when establishing and managing scalable, reliable data pipelines.
  • has knowledge of concepts, processes and tools for cloud-based data management, storage, and AI model deployment.
  • is familiar with AutoML and low-code development tools and their role in the AI industry’s history, distinctive nature, and societal impact.
  • understands the importance of his/her own discipline in a societal and value-creation perspective and the role of data quality in responsible AI.
Skills

The candidate: 

  • can apply vocational knowledge to practical and theoretical problems in collecting, processing and managing structured, semi-structured and unstructured data for AI applications.
  • masters relevant vocational tools, materials, techniques and styles for data ingestion, ETL/ELT processing, orchestration and monitoring of large datasets.
  • can study a situation and identify subject-related issues and what measures need to be implemented, such as optimising or repairing data pipelines.
  • can find information and material that is relevant to a vocational problem, such as documentation for REST or API services, and apply it to solve integration challenges.
  • can explain his/her vocational choices when selecting and deploying cloud-based data services, balancing scalability, security and cost.
  • can find and refer to information and vocational material and assess its relevance when evaluating data quality and ensuring reliable AI performance.
General Competence

 The candidate:

  • can plan and carry out vocational tasks and projects alone or as part of a group and in accordance with ethical requirements and principles, applying data management strategies suited to different industries.
  • can exchange points of view with others with a background in the trade/discipline and participate in discussions about the development of good practice, for example assessing the impact of cloud computing and big data on AI workflows.
  • can build relations with peers, also across discipline boundaries, and with external target groups, communicating data-related insights effectively to various audiences.
  • understands the ethical principles that apply in the field of work and has developed an ethical attitude to responsible data acquisition, storage and processing.
  • can contribute to organisational development by adapting to new technologies and improving data workflows that support AI deployment.
  • can develop work methods, products and services of relevance to practising the discipline, such as efficient, ethical data pipelines and cloud services.
Learning Activities

Digital Learning Resources
The learning management system (LMS) is the primary learning platform where students access most of their course materials. The content is presented in various formats, such as text, images, models, videos or podcasts. Each course follows a progression plan, designed to lead students through weekly modules at their own pace. Exercises and assignments (individual or in groups) are embedded throughout the courses to support continuous practice and assessment of the learning outcomes.

Campus Resources
In addition to the digital learning resources, campus students participate in physical learning activities led by teachers as part of the overall delivery.

Guidance
Guidance and feedback from teachers support students' learning journeys, and may be provided synchronously or asynchronously, individually or in groups, via text, video or in-person feedback.

Reading List

Teaching materials, reading lists, and essential resources will be shared in the learning platform and software user manuals where applicable.