Whether delivered online or onsite, our instructor-led live Kubeflow training programs utilize interactive, hands-on exercises to demonstrate how to leverage Kubeflow for building, deploying, and managing machine learning workflows on Kubernetes.
Kubeflow instruction is offered as either “online live training” or “onsite live training.” The online option, also known as “remote live training,” is facilitated through an interactive remote desktop session. Onsite training can be conducted locally at customer premises in Copenhagen or at NobleProg’s corporate training centers located in Copenhagen.
You can also take bus 26 or 7A and get off around Vesterbros Torv; from there it is a short walk.
By bus
Useful nearby stops include:
Vesterbros Torv (Vesterbrogade) — about 1 minute from the area.
Frederiksberg Allé (Vesterbrogade) — nearby.
Trommesalen (Vesterbrogade) — nearby.
Bus 26 and 7A serve the Vesterbrogade corridor.
København, Copenhagen
NobleProg København, Bredgade 37, København K, denmark, 1260
Near Amalienborg Palace & Royal Theatre.
If you arrive by train
If you arrive at Copenhagen Central Station (København H):
Walk to København H Metro.
Take the M3 or M4 metro toward Kongens Nytorv.
Get off at Kongens Nytorv.
Walk north toward Nyhavn, then continue along Bredgade.
Bredgade 37 is about 500 m / 6 minutes' walk from Kongens Nytorv.
Another easy option
Marmorkirken (Marble Church) Metro Station is also very close to Bredgade 37. The nearby Bredgade area is about 2 minutes' walk from Marmorkirken station.
By bus
There are bus stops around Dronningens Tværgade / Bredgade, so you can also travel by bus and walk the final few minutes.
This hands-on training in Copenhagen 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 following best practices.
This live, instructor-led training in Copenhagen (delivered online or onsite) is designed for developers and data scientists aiming to build, deploy, and manage machine learning workflows on Kubernetes.
Once this training concludes, participants will possess the ability to:
Set up and configure Kubeflow in both on-premise and cloud environments.
Develop, deploy, and control ML workflows that rely on Docker containers and Kubernetes.
Operate full machine learning pipelines across a variety of architectures and cloud platforms.
Utilize Kubeflow to create and manage Jupyter notebooks.
Construct ML training, hyperparameter tuning, and serving workloads that operate across multiple platforms.
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