Chengdu Times Plaza

26/F, 26th Floor, Block A, 2 Sifu Road, Chengdu, China, 610016

The Chengdu Times Plaza Centre is located in one of the most prestigious buildings in the central business district of Chengdu. Home to several Fortune 500 companies working in banking, IT and electronics, car manufacturing and construction, the building is also well located for public transport, hotels and retail outlets. Like the building, the surrounding area is a hub for some of the world's largest corporations with Chengdu's international profile continuing to rise. This is due to the government's promotion of the region in China's Go West policy, which is devoted to stimulating economic activity in western China. The city is rapidly becoming the main financial district in western China. Many hi-tech companies have also begun to settle in Chengdu.

Explore Our Courses

Kubeflow

 35 Hours
This instructor-led, live training in Chengdu (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 Chengdu (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 Chengdu (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 Chengdu (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

2025-05-06 09:30
28 hours
Chengdu Times Plaza

Kubeflow

2025-05-26 09:30
35 hours
Chengdu Times Plaza

Kubeflow on Azure

2025-06-03 09:30
28 hours
Chengdu Times Plaza

Kubeflow on Azure

2025-06-17 09:30
28 hours
Chengdu Times Plaza

Kubeflow on AWS

2025-07-01 09:30
28 hours
Chengdu Times Plaza

Kubeflow on AWS

2025-07-15 09:30
28 hours
Chengdu Times Plaza

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