Online or onsite, instructor-led live Kubeflow training courses demonstrate through interactive hands-on practice how to use Kubeflow to build, deploy, and manage machine learning workflows on Kubernetes.
Kubeflow training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live Kubeflow trainings in Brugge can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Bruges
NH Hotel Bruges, Boeveriestraat 2, Bruges, Belgium, 8000
Bruges became a central port, commercial and financial hub in medieval Europe, linking the countries of the North Sea and the Baltic to the Mediterranean. Wealthy merchants from Bruges traded with those from all over Europe. The first stock exchange in history was born in Bruges in the 13th century. In the 15th century it was the leading financial center in Europe. This economic boom also leads to a cultural and artistic flowering that has left an abundant heritage. It was the most important center for the Flemish primitive painters, who revolutionized Western painting. It has been a member of the Organization of World Heritage Cities since the year 2000. The city even has the distinction of appearing three times on the UNESCO World Heritage List. For its historic center, for its beguinage which is part of the Flemish Beguinages and for its belfry included among the Belfries of Belgium and France. In addition, it is also listed as Intangible Cultural Heritage of Humanity by UNESCO for its Procession of the Holy Blood.
Kubeflow is an open-source platform designed to streamline building, training, and deploying machine learning workloads on Kubernetes.
This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level professionals who wish to build reliable ML workflows using Kubeflow.
Upon completion of this training, attendees will gain the skills to:
Navigate the Kubeflow ecosystem and core components.
Build reproducible workflows with Kubeflow Pipelines.
Run scalable training jobs on Kubernetes.
Serve machine learning models efficiently using Kubeflow Serving.
Format of the Course
Guided presentations and collaborative discussions.
Hands-on labs with real Kubeflow components.
Practical exercises to build end-to-end ML workflows.
Course Customization Options
Customized versions of this training can be arranged to align with your team’s technology stack and project requirements.
This instructor-led, live training in Brugge (online or onsite) is aimed at developers and data scientists who wish 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 on premise and in the cloud using AWS EKS (Elastic Kubernetes Service).
Build, deploy, and manage ML workflows based on Docker containers and Kubernetes.
Run entire machine learning pipelines on diverse architectures and cloud environments.
Using Kubeflow to spawn and manage Jupyter notebooks.
Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.
This instructor-led, live training in Brugge (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to an AWS EC2 server.
By the end of this training, participants will be able to:
Install and configure Kubernetes, Kubeflow and other needed software on AWS.
Use EKS (Elastic Kubernetes Service) to simplify the work of initializing a Kubernetes cluster on AWS.
Create and deploy a Kubernetes pipeline for automating and managing ML models in production.
Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel.
Leverage other AWS managed services to extend an ML application.
This instructor-led, live training in Brugge (online or onsite) is aimed at developers and data scientists who wish 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 on premise and in the cloud.
Build, deploy, and manage ML workflows based on Docker containers and Kubernetes.
Run entire machine learning pipelines on diverse architectures and cloud environments.
Using Kubeflow to spawn and manage Jupyter notebooks.
Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.
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Testimonials (2)
La qualité des explications, et le nombre important de sujets abordés
Hugo SECHIER - Expleo France
Course - Kubeflow on AWS
I enjoyed participating in the Kubeflow training, which was held remotely. This training allowed me to consolidate my knowledge for AWS services, K8s, all the devOps tools around Kubeflow which are the necessary bases to properly tackle the subject. I wanted to thank Malawski Marcin for his patience and professionalism for training and advice on best practices. Malawski approaches the subject from different angles, different deployment tools Ansible, EKS kubectl, Terraform. Now I am definitely convinced that I am going into the right field of application.
Guillaume Gautier - OLEA MEDICAL | Improved diagnosis for life TM
Course - Kubeflow
Provisional Upcoming Courses (Contact Us For More Information)
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