Nagoya - Meieki Minami

5F & 6F Links Meieki Building, 5-31-10 Meieki, Nakamura-ku, Aichi-ken, Nagoya, japan, 450-0002

Nagoya is the third largest city in Japan. This open office is located in the district surrounding Nagoya station. Since 2015, landmark buildings kept inaugurating around the station and by 2017, it is predictable that many corporations will choose to be in this Meieki area. Furthermore, with the magnetic-levitated linear shinkansen to start service in 2027, Nagoya has strategic advantage. Nagoya and Aichi area is known for its auto industry like Toyota. Besides automobiles, there are many head offices of Japanese manufacturers. With their affiliated companies and partners located in the same area, the city itself is a business complex. In recent years, forward-thinking IT companies are also trying to get a foothold in Nagoya. Besides all the attention-gathering news on the redevelopment, Meieki area has good access to Tokyo, Osaka and other major cities. It is also convenient for sales activity and commute. Therefore, it is a popular yet practical business district.

Explore Our Courses

Kubeflow

 35 Hours
This instructor-led, live training in Nagoya (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.
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Kubeflow Fundamentals

 28 Hours
This instructor-led, live training in Nagoya (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.
Read more...

Kubeflow on AWS

 28 Hours
This instructor-led, live training in Nagoya (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.
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Kubeflow on Azure

 28 Hours
This instructor-led, live training in Nagoya (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to Azure cloud. By the end of this training, participants will be able to:
  • Install and configure Kubernetes, Kubeflow and other needed software on Azure.
  • Use Azure Kubernetes Service (AKS) to simplify the work of initializing a Kubernetes cluster on Azure.
  • 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.
Read more...

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Testimonials (1)

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

Upcoming Courses

Kubeflow Fundamentals

2025-05-12 10:00
28 hours
Nagoya - Meieki Minami
¥ 911088 ~ (Online)
¥ 1822176 ~ (Classroom)

Kubeflow

2025-05-26 10:00
35 hours
Nagoya - Meieki Minami
¥ 1138860 ~ (Online)
¥ 2277720 ~ (Classroom)

Kubeflow Fundamentals

2025-06-09 10:00
28 hours
Nagoya - Meieki Minami
¥ 911088 ~ (Online)
¥ 1822176 ~ (Classroom)

Kubeflow

2025-06-23 10:00
35 hours
Nagoya - Meieki Minami
¥ 1138860 ~ (Online)
¥ 2277720 ~ (Classroom)

Kubeflow on AWS

2025-07-07 10:00
28 hours
Nagoya - Meieki Minami
¥ 911088 ~ (Online)
¥ 1822176 ~ (Classroom)

Kubeflow Fundamentals

2025-07-21 10:00
28 hours
Nagoya - Meieki Minami
¥ 911088 ~ (Online)
¥ 1822176 ~ (Classroom)

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