Machine Learning into Practice: Deep Dive into MLOps
Join this 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, in a reliable and efficient way, machine learning models in production . The word is a compound of "machine learning" and the continuous delivery practice (CI/CD) of DevOps in the software field.
Praktische info:
Leertraject
Inschrijven?
- Voorwaarden: programming skills (preferably Python), foundational understanding of machine learning concepts
- Prijs: €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)
- 23 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, machine learning efficiënt ingezet
Eenmaal je AI kan ontwikkelen, is het nog zaak om die AI-modellen op een efficiënte manier te implementeren, in de dagdagelijkse praktijk: zelflerend, zelfsturend, maar toch gecontroleerd. Dat bereik je dankzij MLOps: een bundel van goede praktijken waarmee je het volledige AI-proces kan automatiseren en optimaliseren. Axel-Jan Rousseau (UHasselt, VAIA) legt uit hoe dit werkt.
Lesgevers / sprekers
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.
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
Senior Data Engineer @Lighthouse
- Data specialist
- PhD in Artificial Intelligence
- Passionate and certified teacher
- Allround pianist
Sam Leroux
Dr. ing. Sam Leroux behaalde in juli 2014 summa cum laude een Master of Science diploma in Information Engineering Technology aan de Universiteit Gent, België. In september van dat jaar ging hij aan de slag bij het departement Informatietechnologie aan de Universiteit Gent. Na het behalen van zijn doctoraat in 2019 werkt hij als postdoctoraal onderzoeker in het Distributed Machine Learning team bij IDLab Gent. Zijn belangrijkste onderzoeksinteresses zijn energie-efficiënt deep learning, edge computing en Tiny ML. Sam Leroux geeft ook verschillende cursussen in zowel de bachelor- als de masteropleiding Information Engineering Technology.
Expertise: Machine learning, Deep learning, Edge computing, resource efficient machine learning, TinyML
Senior AI Product Engineer @ Bizzy - Building the sales utopia, one lead at a time.
Thomas doet aan Odisee Hogeschool toegepast onderzoek naar Data & AI. Zijn centrale vraag: hoe zetten organisaties AI op een verantwoorde en efficiënte manier in? Als medeoprichter van Webstrive combineert hij dat onderzoek met de dagelijkse praktijk, en vertaalt hij complexe AI-concepten naar concrete toepassingen voor de werkvloer. Hij heeft daarbij oog voor strategie, privacy en de noden van de eindgebruiker, in lijn met GDPR en de EU AI Act.
Daarnaast geeft Thomas workshops die de volledige cyclus van een AI-project doorlopen: van strategische sessies over prototyping tot validatie. In de prototyping-workshops bouw je in enkele uren een werkend prototype met vibe coding-tools zoals Lovable, v0 en Bolt. In andere sessies ontdek je hoe AI-agents je dagelijkse werk versnellen: Claude Code voor wie ontwikkelt, Claude Cowork voor wie documenten en bestanden beheert.
Thomas verzorgde al opleidingen en workshops voor onder andere UGent, VOKA, Unizo, Embuild en Etion. Elke sessie wordt afgestemd op sector en kennisniveau, gaande van een eerste kennismaking met prompt engineering tot meerdaagse trajecten waarin teams hun eigen AI-case uitwerken.
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.
Jens Krijgsman
Jens Krijgsman is een onderzoeker met uitgebreide kennis van zowel front-end- als back-end-development. Daarnaast heeft hij een achtergrond in software-architectuur. Hij houdt zich bezig met automatisering en AI-integratie in uiteenlopende projecten. Deze variëren van computervisiemodellen tot de nieuwste LLM's, waarbij MLOps-, AIOps- en DevOps-tools worden ingezet in combinatie met oplossingen op maat. Jens is een van de teamleads binnen het AI Lab aan Howest.
Expertise:
MLOps, fullstack development, CI/CD, gebruik van LLM's, RAG, finetuning, software engineering
Data Engineer at Dataminded
