Teaching
I am the main lecture on course 02476 Machine Learning Operations at Technical University of Denmark, having developed most of the material myself.
Webpage: https://skaftenicki.github.io/dtu_mlops
DTU course page: https://kurser.dtu.dk/course/02476
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.
Supervision
Feel free to reach out to me if you have an interesting project proposal and is missing a supervisor.
Current students:
| Year | Students | Project |
|---|---|---|
| 2024 | Mikkel Theiss Westermann | |
| Alignment of Unstructured Data with Structured Models in Accounting Software Using Artificial Intelligence |
Former students:
| Year | Students | Project |
|---|---|---|
| 2024 | Jacob August Ottensten] and Gustav Franck | Automatic Label Error Detection in MLOps Pipelines |
| 2024 | Nima Taghidoust | Harnessing LLMs for Customer Support and Financial Inquiry Automation |
| 2024 | Navaneeth Kizhakkumbadan Pacha | Investigating Deep Learning based methodologies to automate program Synthesis for Biostatistics ADAM programming |
| 2024 | Mathias Kristensen | Cloud resource cost optimisation for machine learning model training using Kubeflow in Kubernetes Clusters |
| 2024 | Freya Gerup Helstrup | A framework for automated testing of ETL pipelines |
| 2024 | Vivian Jacobsen | Generative AI of flexible reports |
| 2024 | Kristóf Kenéz Drexler | Locus: Global Localization of Earthly Images powered by Deep Learning |
| 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 |
| 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 |