Conducted either online or onsite, our instructor-led live Kubeflow training courses provide an interactive, hands-on experience in building, deploying, and managing machine learning workflows on Kubernetes. Through practical exercises, you will gain the skills necessary to effectively utilize Kubeflow in real-world scenarios.
You can choose between "online live training" and "onsite live training" options. Online live training, also referred to as "remote live training," is delivered through an interactive, remote desktop session. Alternatively, onsite live training can be hosted locally at your customer premises in Prague or at NobleProg’s corporate training centers in Prague.
From Prague Main Train Station (Praha hlavní nádraží)
Take tram 9 from Hlavní nádraží toward Sídliště Řepy.
Get off at Újezd.
Walk about 10 minutes toward Malostranské náměstí / Prokopská.
Continue along Prokopská to 296/8.
Alternative: Take the metro C from Hlavní nádraží → Muzeum, change to metro A → Malostranská, then walk across Malá Strana. This involves more walking.
From Prague Bus Station — Florenc
Take metro B from Florenc toward Zličín.
Get off at Můstek.
Change to metro A toward Nemocnice Motol.
Get off at Malostranská.
Walk approximately 10–15 minutes to Prokopská 296/8.
This hands-on training in Prague provides the essential skills needed to build, train, and serve machine learning models on Kubernetes using Kubeflow. You will learn to navigate the ecosystem, design scalable pipelines, and manage production-ready workloads while adhering to industry best practices.
This live, instructor-led training conducted in Prague (either online or onsite) targets developers and data scientists aiming to develop, deploy, and manage machine learning workflows on Kubernetes.
Upon completion, participants will be equipped to:
Install and configure Kubeflow in on-premise and cloud environments.
Construct, deploy, and manage ML workflows using Docker containers and Kubernetes.
Run full machine learning pipelines across diverse architectures and cloud setups.
Utilize Kubeflow to generate and manage Jupyter notebooks.
Build ML training, hyperparameter tuning, and serving workloads across various platforms.
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