Machine Learning into Practice: Deep Dive into MLOps
16, 17 & 18 December 2024
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.
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.
This course is a collaboration between UGain and VAIA.
Data scientists working at companies looking to acquire the skills needed to deploy their models to robust and scalable applications.
You will be working with your own laptop. This must be powerful enough (minimum 8GB RAM) and participants must have administrative rights to install the necessary programs.
Participants can obtain a certificate of attendance.
- Gilles Ballegeer, Vintecc
- Sander Borny, Ghent University
- Cedric De Boom, Dataminded
- Dimitri De Rocker, Datashift
- Sam Leroux, Ghent University
- Joseph Miano, Superlinear
- Merlijn Sebrechts, Ghent University
- Thomas Van den Bossche, Odisee
Programme
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.
Data logging for industrial edge devices (1h)
Having a good data logging setup is crucial for developing industrial machine vision algorithms that run on the edge.In this sessions we present how we set up our current projects and show several use-cases to demonstrate the effectiveness.
Teacher: Gilles Ballegeer
Applying DevOps to Machine Learning (4h)
In this hands-on workshop we offer an introduction to MLOps, focusing on how DevOps practices can be applied to machine learning. Together, we will work through a mini project where we will deploy, serve, scale, and monitor a machine learning model in production. The session covers key topics such as model deployment, versioning, scaling, and continuous training. It also addresses important aspects of monitoring and dealing with concept drift. Depending on time and interest, optional topics like A/B testing and LLMOps may also be included. Throughout the workshop, participants will be introduced to recommended tools and best practices relevant to MLOps.
Teacher: Cedric De BoomAI assistants: from POC to production (3h)
AI assistants are being widely adopted in businesses as a means for efficient context-aware interaction. While setting up a custom AI assistant is not rocket science, different challenges exist to get it up and running in a production environment. In this workshop we will discuss these challenges and set up our own RAG-based solution in the cloud. We will zoom in on tools and frameworks that can be used for traceability and automated quality monitoring.
Teacher: Dimitri De Rocker
ML model optimizations for efficient edge AI deployment (1h)
State-of-the-art machine learning 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 lesson, 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 LerouxFine-Tuning Small LLMs for PII Detection and Secure Edge Deployment (3h)
You wil learn how to fine-tune small language models for detecting Personally Identifiable Information (PII), with a strong focus on data privacy and secure edge deployment. You'll learn to generate synthetic data for training without exposing real sensitive information and perform local fine-tuning entirely on-device to eliminate cloud-related risks. We'll cover best practices for securely deploying your model on edge devices, including implementing encryption methods. The session also explores testing strategies to validate your model's PII detection accuracy while upholding security standards. By the end, you'll be equipped to build and deploy a privacy-focused AI application that operates offline on an edge device, safeguarding sensitive information throughout its lifecycle.
Teacher: Joseph MianoIntroduction to Federated Learning with Python & Flower (2h)
In this session 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
Practical info
Fee
- 16 December 2024 . . . . . . . . . . . . . . . € 465,-
- 17 December 2024 . . . . . . . . . . . . . . . € 465,-
- 18 December 2024 . . . . . . . . . . . . . . . € 400,-
- Complete course . . . . . . . . . . . . . . . . . € 1.200,-
Payment occurs after reception of the invoice.
All invoices are due in thirty days. All fees are exempt from VAT.
Reduction
When a participant of a company subscribes for the complete course, a reduction of 20% is given to all additional subscriptions from the same company. In that case, only one invoice is issued per company.Special prices for Phd-students. For further information, please send us an email.
Cancellation policy
Cancellation must be done in writing. Our cancellation conditions can be consulted on www.ugain.ugent.be/cancellationTraining vouchers
Ghent University accepts payments by KMO-portefeuille (www.kmo-portefeuille.be; authorisation ID: DV.O103194).Opleidingsverlof (VOV)
This course has too few contact hours to qualify for VOV.