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 training can be carried out locally on customer premises in Taipei or in NobleProg corporate training centers in Taipei.
AThe Regus Aurora Business Centre is located in the heart of Neihu Science Park, a popular choice for high-tech companies to ...
AThe Regus Aurora Business Centre is located in the heart of Neihu Science Park, a popular choice for high-tech companies to set up their headquarters. Major corporates in this area are involved in telecommunications, IT, electronics manufacturing, media, retail and logistics. Companies include Honhai, Compal, AUO, BENQ,, FET, Advantech, MITAC, DELTA and D-Link. The area is well supported by a selection of restaurants, hotels, retail malls and banks. The centre has easy access to many means of public transportation including MRT, taxi and bus. Xihu station on the Neihu Line and the bus station are within a one minute walk from the centre. The central location and amenities make Regus Aurora Business Centre an excellent choice for international and domestic companies looking for flexible and fully equipped office space at a reasonable price.
Taipei- Concord
8/F, No.367, Fuxing N. Rd., Taipei, taiwan, 10543
The Taipei Concord business centre is located in the north of SongSan district, close to main roads Fuxing N Road, Minquan Ea...
The Taipei Concord business centre is located in the north of SongSan district, close to main roads Fuxing N Road, Minquan East Road and Minsheng East Road, serves as an important spot linking office buildings and financial organizations. Surrounded by Rongxing Garden, Sherwood Hotel, Taipei University and other landmarks, with many restaurants and shopping in the immediate vicinity. Taipei Concord Building business centre has a great location, with rapid access to all other Taipei districts by public transportation: MRT, taxi and bus. It is only 1 minute walk to Zhongshan Junior High School Station in MRT Neihu Line. There are also many bus stops nearby with direct and easy access to Neihu, Sanchung, Taoyuan and other places. It is also within 3 minutes of Songshan airport and 40 minutes to Taoyan International airport. The Taipei Concord Building centre offers a perfect solution for local and overseas companies who want to start or grow their businesses in Taipei, who are looking for flexible and fully-equipped offices with comprehensive services at a reasonable price.
Taipei Walsin Xinyi
11/F, 1 Songzhi Road, Taipei, taiwan, 11047
The office space in the Walsin Xinyi Building is in an A-grade development located at the heart of Xinyi CBD, a new business ...
The office space in the Walsin Xinyi Building is in an A-grade development located at the heart of Xinyi CBD, a new business district where an increasing number of multi-national corporations choose to locate their offices. The building is surrounded by many new hotels, restaurants and high-end shopping malls such as W Hotel, Le Meridian, Shinkong Mitsukoshi Department Store and Bellavita, a high-end shopping mall. Walsin Xinyi Building provides a balance of location and building quality, with a good multi-national tenant mix. Citibank and Mitsui Japanese Restaurant are two of the many tenants of the building. The office space at Walsin Xinyi Building is only a 5-minute walk to Taipei 101 and a 2-minute walk to the Taipei City Hall subway (MRT) station.
This instructor-led, live training in Taipei (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 Taipei (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 Taipei (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.
This instructor-led, live training in Taipei (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to Google Cloud Platform (GCP).
By the end of this training, participants will be able to:
Install and configure Kubernetes, Kubeflow and other needed software on GCP and GKE.
Use GKE (Kubernetes Kubernetes Engine) to simplify the work of initializing a Kubernetes cluster on GCP.
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 GCP services to extend an ML application.
This instructor-led, live training in Taipei (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to IBM Cloud Kubernetes Service (IKS).
By the end of this training, participants will be able to:
Install and configure Kubernetes, Kubeflow and other needed software on IBM Cloud Kubernetes Service (IKS).
Use IKS to simplify the work of initializing a Kubernetes cluster on IBM Cloud.
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 IBM Cloud services to extend an ML application.
This instructor-led, live training in (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to an OpenShift on-premise or hybrid cloud.
By the end of this training, participants will be able to:
Install and configure Kubernetes and Kubeflow on an OpenShift cluster.
Use OpenShift to simplify the work of initializing a Kubernetes cluster.
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.
Call public cloud services (e.g., AWS services) from within OpenShift to extend an ML application.
This instructor-led, live training in Taipei (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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