Machine Learning into Practice: Deep Dive into MLOps
This foundational course offers a comprehensive journey through the various stages of deploying and maintaining machine learning models to applications using the MLOps paradigm.
MLOps is a paradigm that aims to deploy and maintain machine learning models in production reliably and efficiently. The word is a compound of "machine learning" and the continuous delivery practice (CI/CD) of DevOps in the software field.
Practical information:
Want to register?
- Prerequisites: programming skills (preferably Python), foundational understanding of machine learning concepts
- Price: €400-€465 per session or €1200 for the full course
Bring your own laptop, with:
- enough power (minimum 8GB RAM)
- administrative rights to install the necessary programs
Classes:
- 22 September 2025 (9h -17.30h)
- 23 September 2025 (9h - 17.30h)
- 24 September 2025 (9h - 16h) + closing reception
Georganiseerd door:
Through hands-on workshops participants will gain insights into the core steps of MLOps: Data preparation and versioning, model deployment, monitoring, scaling, and continuous training. They will understand the significance of having a clear understanding of what to expect in real-world scenarios when deploying a machine-learning model.
Additionally, this workshop covers the specific challenges of deploying LLMs and RAG solutions. We conclude the workshop with techniques for downscaling models to edge devices for real-time processing.
Throughout the course, participants will acquire practical skills and knowledge essential for navigating ML deployment smoothly, empowering them to face various real-world challenges.
Programme
Day 1 - 22 September 2025
Workshop: Docker & Kubernetes (4 x 1,5h)
This workshop will give you practical hands-on experience with using Docker and Kubernetes for MLOps. It will also give you the theoretical foundations to make you comfortable with relying on Docker and Kubernetes for MLOps.
- Docker basics.
- Using Docker for ML training.
- How to migrate from Jupyter Notebooks to production-ready docker containers.
- GPU acceleration in Docker containers.
- How to deploy production ML models on Kubernetes.
- Upgrading production ML models in Kubernetes.
Teachers: Sander Borny, Tom Goethals & Bruno Volckaert
Data and AI Pipelines and Version Control (1h)
AI projects require a lot of data. This data needs to be processed specifically for the requirements of the AI model architecture. During development, this often changes quite a lot, as the requirements grow and problems are fixed. Developers often want re-usable pipelines that are version tracked with the rest of their code.
We will check out some of the interesting orchestration tools for these preprocessing pipelines, and how to keep track of the artefacts generated in between different steps.
What are the best practices? What are the hurdles to overcome? How to set up a good pipeline that's easy for developers, but provides good quality for all stakeholders?
Using Kubeflow as a pipeline orchestrator tool on Kubernetes, we will explain how to set up version control for data and AI pipelines in a good way.
Teacher: Nathan Segers & Jens Krijgsman
Day 2 - 23 September 2025
Applying DevOps to Machine Learning (4h)
This four-hour, hands-on workshop offers an introduction to MLOps,
with an emphasis on how DevOps principles can be applied to machine
learning. Together we will work on a mini-project in which we bring a
machine learning model into production via a public cloud provider
(AWS).
We will also cover important topics such as continuous integration
& continuous deployment (CI/CD), backend-frontend communication,
containerization, etc. We will briefly discuss how to deal with
security, scaling, A/B testing, and concept drift. Throughout the
session, recommended tools and best practices for MLOps will be
discussed.
Teacher: Cedric De Boom (Lighthouse)
Workshop RAG Models (3h)
The training content focuses on both the theory and practice of
Retrieval-Augmented Generation (RAG) and deploying AI applications. We
begin with a theoretical introduction to how RAG works, followed by a
hands-on workshop to apply this knowledge. Next, we explore how such
systems are deployed, with attention to observability, monitoring, and
quality assurance.
The training provides insight into how to evaluate models and
systems. Finally, we address key challenges such as monitoring and the
security of LLM models, including aspects of red teaming. This balanced
mix of theory and practice makes the training suitable for a broad
audience interested in AI applications.
Teacher: Rushil Daya (Dataminded)
Day 3 - 23 September 2025
ML model optimizations for efficient edge AI deployment (1h)
State-of-the-art machine learning (ML) models for image recognition and natural language processing require a huge amount of computational resources that are not available on resource constrained edge devices. In this talk, we will discuss the different options that are available to optimize these models, reducing their computational cost and memory footprint, making it possible to use them on mobile and embedded devices.
Teacher: Sam Leroux (IDLab, Ghent University, imec)
Fine-Tuning Small LLMs for PII Detection and Secure Edge Deployment (3h)
In this session, you’ll learn how to fine-tune small language models to detect Personally Identifiable Information (PII), with a strong emphasis on data privacy and secure edge deployment. We'll guide you through generating synthetic training data to avoid exposing real sensitive information and demonstrate how to perform local, on-device fine-tuning.
You’ll also explore best practices for securely deploying models on edge devices while maintaining stringent security standards, including techniques for implementing robust encryption.
By the end, you’ll be equipped to develop and deploy a
privacy-preserving AI application that runs entirely offline on an edge
device, ensuring sensitive information remains protected throughout its
lifecycle.
Teacher: Robbe de Sutter (Superlinear)
Introduction to Federated Learning with Python & Flower (2h)
In this workshop you will be introduced to Federated Learning in a
practical way, a technique that allows you to train machine learning
models without centrally storing the data. We will cover the basic
principles, look at available frameworks and discuss the challenges of
this technology. In the second, hands-on part you will get started with
the Flower framework and Python to apply this knowledge in practice.
Teacher: Thomas Van den Bossche (Odisee)
MLOps: using machine learning efficiently
Do you understand how AI works? Then now is the time to learn how to implement AI models efficiently, in day-to-day practice: self-learning, self-directed, yet controlled. You can achieve this thanks to MLOps: a bundle of good practices that allows you to automate and optimise the entire AI process. Axel-Jan Rousseau (UHasselt, VAIA) explains how this works.
Teachers / speakers
Sander Borny
Sander Borny received the M.Sc. degree in information engineering technology from Ghent University, in 2016. He is currently a Research Assistant with IDLab, Ghent University - imec. His focus is providing cloud infrastructure, DevOps and DataOps to various projects in the fields of smart cities, smart grids, and the Internet of Things.
Tom Goethals
Senior researcher cloud & services computing at IDLab - UGent
Bruno Volckaert
Bruno Volckaert is professor op vlak van geavanceerde software engineering en veilige gedistribueerde systemen aan de vakgroep Informatietechnologie (INTEC) van de Universiteit Gent en de imec IDLab-groep. Zijn huidige onderzoek richt zich op betrouwbare en performante gedistribueerde software voor onder andere schaalbare data-invoer en -verwerking, schaalbare en veilige architecturen voor AI en optimalisatie van cloudgebaseerde applicaties. Hij heeft meegewerkt aan het tot stand komen en uitvoeren van meer dan 65 (inter)nationale onderzoeksprojecten en is auteur of co-auteur van meer dan 200 peer-reviewed artikelen in internationale tijdschriften en conferenties.
Expertise: AI enablers - Cloud/Edge, Containerisatie, Kubernetes, Cybersecurity
Cedric De Boom
Senior Data Engineer @ Dataminded
- Data specialist
- PhD in Artificial Intelligence
- Passionate and certified teacher
- Allround pianist
Sam Leroux
Dr. ing. Sam Leroux obtained a Master of Science degree in Information Engineering Technology summa cum laude from Ghent University, Belgium, in July 2014. In September of that year, he joined the Department of Information Technology at Ghent University. After obtaining his Ph.D in 2019, he is working as a postdoctoral researcher in the Distributed Machine Learning team at IDLab Ghent. His main research interests are energy efficient deep learning, edge computing and Tiny ML. Sam Leroux also teaches various courses in both the bachelor and master of Science in Information Engineering Technology programme.
Expertise: Machine learning, Deep learning, Edge computing, resource efficient machine learning, TinyML
Robbe De Sutter
Robbe De Sutter is a GenAI Lead at Superlinear, combining technical depth with leadership. He leads a team of ML engineers, driving delivery of AI/ML solutions for real-world impact. With a background in Information Engineering Technology, he helps translating machine learning ideas into scalable applications.At Superlinear, Robbe has built and deployed AI products across different sectors. For example: he led the creation of an LLM assistant for pharmaceutical validation at Agidens, minimising errors and accelerating project delivery. At Brussels Airport he architected a forecasting engine to optimize operations reducing waiting times and improving passenger satisfaction.He contributes to projects at the frontier of GenAI, including a threat scan of state-of-the-art Deepfake technology for NVISIO and helps realise a mature MLOps infrastructure for APICA across multiple production-grade applications at the Port of Antwerp Bruges.
🧠AI Systems & Leadership – Team Lead at Superlinear, mentoring AI engineers and aligning delivery with business priorities.
📊 Applied Machine Learning – Delivered LLMs and AI systems across different sectors.
🔍 GenAI Research – Analyzed Deepfake threats and GenAI risks for enterprise awareness.
🛠️ MLOps & Deployment – Owns deployment pipelines and cloud orchestration (AWS/Azure) for scalable AI delivery.
Thomas Van den Bossche
Thomas conducts applied research on Data & AI at Odisee University College, focusing on one central question: how can organizations deploy generative AI responsibly and efficiently? Combining research experience with practical experience as co-founder of Webstrive, he translates complex AI concepts into concrete applications.
Thomas looks beyond the technology. He guides organizations through the strategic, ethical, and privacy aspects of AI implementation. This helps companies not only quickly adopt AI but also do so sustainably and responsibly.
In addition to consulting, Thomas also provides customized training courses tailored to the sector and knowledge level for Ghent University, VOKA, Unizo, Embuild, and other organizations.
Nathan Segers
Als onderzoeker bij het AI Lab van Howest, docent AI en XR in de opleiding Multimedia en Creatieve Technologie en de Engelstalige bachelor Creative Technologies and AI werkt Nathan aan tal van projecten om AI-modellen te integreren in software. AI projecten operationeel brengen, gecombineerd met eventuele XR interfaces behoort tot zijn expertise.
Daarnaast werkt Nathan ook mee aan XR projecten binnen de AV-sector, waar nodig gecombineerd met AI.
Expertise: MLOps, Mixed Reality Design
Jens Krijgsman
Jens Krijgsman is a researcher with extensive knowledge of both front-end and back-end development, and a background in software architecture. His profile has led him to handle the automation and integration of AI in various projects. This ranging from computer vision models to the latest LLMs where MLOps, AIOps, and DevOps tools are used together with custom solutions. Jens serves as one of the team leads at Howest's AI Lab.
Expertise: MLops, Fullstack, CI/CD, Deploying LLM, RAG, Finetuning, Software Engineering
Rushil Daya
Data Engineer at Data Minded
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