The module is aimed at participants who are already in the workforce within audio software engineering, embedded systems, hearing-aid technology, and telecommunications. 

The module integrates concepts of workflow engineering and intelligent orchestration, applied in AI Engineering context to the need of modular, transparent, and automated model management pipelines in high-performance and high-reliability settings, for deploying and maintaining AI-based products and enterprise solutions.

Knowledge

• Have an advanced theoretical knowledge of MLOps principles, focusing on audio-specific machine learning workflows
• Have an understanding of the technical and organizational challenges in deploying AI/ML models within the audio industry
• Have knowledge about how to prepare for real-world constraints like latency, reliability, and data drift
  gain a deep understanding of lifecycle management and ethical considerations in deploying machine learning in industry settings

Skills

• You will be able to develop and manage audio ML pipelines by incorporating best practices in model training, deployment, and monitoring
• You will be able to perform robust feature extraction for audio datasets, using advanced tools such as TensorFlow, Kubernetes, and Docker, and evaluate the environmental impacts of these workflows 
• You will be able to communicate complex ML engineering concepts and solutions effectively to both technical and non-technical stakeholders.

Competences

• You will be able to demonstrate autonomy and responsibility in managing complex MLOps projects for audio-based systems, integrating knowledge from multiple disciplines (signal processing, machine learning, and systems design)
• You will be able to develop collaborative skills by working within interdisciplinary teams and integrating input from audio engineers, AI researchers, and business managers
• You will be able to engage in continuous learning and reflection to adapt MLOps workflows to evolving audio industry trends, including sustainability and ethical AI concerns.

It is expected that the module will be taught in lectures, classroom instruction, workshops and exercises (individually and in groups) and reflection.

The module is assessed by a written exam.

The module is affiliated with the Section for Media Technology at Aalborg University, Department of Architecture, Design and Media Technology, and it will be taught by researchers and lecturers in this field.

Read more about the module Signal Processing for AI Engineering

Read about the Study Board of Media Technology

Seminar dates

• 04.11.2026 at 08:15 - 11:45
• 11.11.2026 at 08:15 - 11:45
• 18.11.2026 at 08:15 - 11:45
• 25.11.2026 at 08:15 - 16:15

Exam: Written exam in November/December

Aalborg University, Campus Copenhagen

Address:

• A.C. Meyers Vænge 15, 2450 København SV,
  Augmented Performance Lab Building A 2.0.023

In addition to the participation fee, you should expect expenses for literature, meals, transportation, and possibly accommodation.

Mandatory food expenses is DKK 425.

To be admitted to the course, you must have a relevant bachelor’s degree, professional bachelor’s degree or master’s degree and requires a minimum of two years relevant work experience obtained after the completion of the qualifying degree.

The module assumes knowledge within Audio DSP, Machine Learning, AI or similar areas 

Recommended prerequisite for participation in the module

• Basics of Audio Signal Processing, Machine Learning, and AI Programming in python / MATLAB.

Prerequisite for participation in the module

• AI Programming in python / MATLAB.

We admit a minimum of 10 and a maximum of 15 students on a first-come, first-served basis.

Download login and application guide

• Associate Professor Cumhur Erkut

Study secretaries

• Nicola Walker. Email: evu-create@create.aau.dk
• Trine Lund Sørensen. Email: evu-create@create.aau.dk 

Contact Continuing Education Office:

• Email: efteruddannelse@aau.dk
• Phone: 99 40 94 20