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I am the main lecture on course 02476 Machine Learning Operations at Technical University of Denmark, having developed most of the material myself.


DTU course page:

The course introduces students to a number of coding practices that will help them organization, scale, monitor and deploy machine learning models either in a research or production setting. To provide hands-on experience with a number of frameworks, both local and in the cloud, for doing large scale machine learning models.


Feel free to reach out to me if you have an interesting project proposal and is missing a supervisor.

Current students:

Year Students Project
2023 Róbert Gers High performance extraction of financial market data
2023 Andreas Piper Mårtensson Investigation and implementation of Machine Learning Pipelines within the area of image analysis and classification
2023 Tamas Janos Paulik Implementation and orchestration of a scalable and automated Machine Learning Operation pipeline
2023 Stefanos Rodopoulos Exploring Bio relationships through Counterfactual Optimisation Analysis using VAE
2023 Joshua Sebastian Using Deep Learning and MLOps to detect Dementia through Speech Analysis
2023 Alejandro Rodriguez Salamanca Memory Efficient Methods for Large Models
2023 Anna Bzinkowska Machine Learning approach to product categorization in the manufacturing industry
2023 Melina Siskou Optimizing Machine Learning Operations in Logistics: Exploring Best Practices and Evaluating Tools
2023 Milad Taghikhani Exploring Multimodal Data Integration to Large Language Models and LLM-based generative models

Former students:

Year Students Project
2023 Thomas Spyrou Monitoring black-box classification models in machine learning systems
2023 Laurine M Dargaud Developing a spontaneous speech-based machine learning model for the early detection of dementia
2023 Spyridon Vlachospyros Context-aware object detection using deep learning
2023 Jonah Jad Tabbal AutoML and Meta-learning through data science competitions
2023 Mads Dudzik Møller MLOps in a Deep Learning enabled production environment
2023 Joachim Andersson and Jonas Hoffmann Exploring the Extensions and Limitations of Metadata Archaeology via Probe Dynamics
2023 Julius Radzikowski and Carl Schmidt Using meta-labeled data to improve deep-learning classification model robustness, and boost data quality
2022 Simon Moe Sørensen Deep multimodel modelling of images and text from wine
2022 Victor Girardin Flindt Fault detection in industrial production processes using Deep learning methods
2022 Jakub Wladyslaw Szreder Creating a Machine learning pipeline for training and evaluation of models for medical data
2022 Nicolai Weisbjerg Quantifying Uncertainty in Semantic Segmentation using Bayesian Deep Metric Learning
2022 Frederik Kjær and Jonas Christian Rask Levin Construction of a Recommendation system for best next buy recommendations
2022 Bekarys Gabdrakhimov End-to-end machine learning project on classification of EEG signal of depressed patients
2022 Xianhao Liu Continues piecewise affine based normalizing flows
2022 Andri Geir Arnarson and Asger Frederik Græsholt Automation of a fruit sorting system
2022 Gustav Selfort Hartz Using deep learning and transformer models for processing, searching, and tagging legal documents
2022 Marco Placenti Machine Learning Pipeline Engineering with Amazon Web Services
2021 Frederik Kromann Hansen and Jonas Søbro Christophersen Investigating osteoarthritis via x-ray images using deep learning
2021 Morten Holm Thomsen and Simon Kristian Jacobsen Development of Graph Neural Networks for prediction of financial data
2020 Kathrine Thorup Hagedorn Entertainment Recommender System complying to the GDPR ruleset
2020 Dominik Mate Kovacs and Zoltán Kovács Generating and Detecting Deepfakes
2018 Julie Liv Cetti Hansen Foci detection in cells using deep learning