Delivered online or onsite, these instructor-led live Kubeflow training courses provide interactive, hands-on experience in utilizing Kubeflow to construct, deploy, and manage machine learning workflows on Kubernetes.
Kubeflow training can be accessed as either "online live training" or "onsite live training". Online live training (also referred to as "remote live training") is facilitated through an interactive remote desktop. Onsite live training may be conducted locally at customer premises in Gaia or at NobleProg corporate training centers in Gaia.
NobleProg -- Your Local Training Provider
Holiday Inn Porto - Gaia
220, Rua Diogo Macedo, 220, Vila Nova de Gaia 4400-107 , Gaia, Portugal
From Porto by Metro
If you're coming from Porto city centre:
Take the Metro Line D (yellow line) toward Vila d’Este.
Get off at João de Deus.
From João de Deus, walk about 13 minutes to: Rua Diogo Macedo 220, 4400-107 Vila Nova de Gaia.
From Vila Nova de Gaia train station
If by “local train station” you mean Vila Nova de Gaia-Devesas:
The address is approximately 12 minutes walking from Vila Nova de Gaia station according to the accommodation's location information.
You can therefore walk directly rather than taking the metro.
This practical training in Gaia provides you with the proficiency necessary to build, train, and serve machine learning models on Kubernetes using Kubeflow. You will master navigating the ecosystem, designing scalable pipelines, and overseeing production-ready workloads while adhering to industry best practices.
This instructor-led, live training in Gaia (online or onsite) is designed for developers and data scientists aiming to build, deploy, and manage machine learning workflows on Kubernetes.
By the end of this training, participants will be able to:
Install and configure Kubeflow in on-premise and cloud settings.
Build, deploy, and manage ML workflows using Docker containers and Kubernetes.
Run entire machine learning pipelines across diverse architectures and cloud environments.
Use Kubeflow to create and manage Jupyter notebooks.
Develop ML training, hyperparameter tuning, and serving workloads across multiple platforms.
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