Delivered either remotely or in person, these instructor-led Kubeflow courses offer hands-on, interactive experience to demonstrate how to leverage Kubeflow for building, deploying, and managing machine learning workflows on Kubernetes.
Kubeflow training is offered in two formats: online live training or onsite live training. Online live training (also referred to as remote live training) is conducted through an interactive remote desktop session. Onsite live training can be arranged at your local premises in Lille or within NobleProg's corporate training facilities in Lille.
NobleProg -- Your Local Training Provider
Lille, Gare Flandres
NobleProg Lille, 21 Avenue le Corbusier, Lille, france, 59800
In front of Flandres TGV Train Station
From Lille train stations
From Lille-Flandres: the address is essentially right next to the station — about 1–3 minutes on foot.
From Lille-Europe: walk toward Lille-Flandres along Avenue Le Corbusier. It is roughly 5–10 minutes on foot.
So you don't need a bus or metro if you're arriving by train.
If you're coming by bus
The nearest bus stops to Avenue Le Corbusier include Lion d'Or and Jacquet. Several local lines serve the area, including 13, 86, L5 and L91.
This practical training in Lille provides the necessary skills to build, train, and serve machine learning models on Kubernetes using Kubeflow. You will learn to navigate the ecosystem, author scalable pipelines, and manage production-ready workloads by adhering to industry best practices.
This live, instructor-led training, delivered in Lille (either online or onsite), is designed for developers and data scientists looking to build, deploy, and manage machine learning workflows on Kubernetes.
By the end of this session, participants will be capable of:
Installing and configuring Kubeflow in on-premise and cloud environments.
Building, deploying, and managing ML workflows that rely on Docker containers and Kubernetes.
Running full machine learning pipelines across diverse architectures and cloud platforms.
Using Kubeflow to create and manage Jupyter notebooks.
Developing ML training, hyperparameter tuning, and serving workloads across various platforms.
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