Kubeflow 교육

Kubeflow 교육

현지 강사 주도 라이브 Kubeflow 교육 과정은 대화식 실습을 통해 Kubeflow 를 사용하여 Kubernetes 에서 기계 학습 워크 플로를 구축, 배포 및 관리하는 방법을 보여줍니다. Kubeflow 교육은 "현장 라이브 교육"또는 "원격 라이브 교육"으로 제공됩니다. 현장 라이브 교육은 고객 구내에서 로컬로 수행 할 수 있습니다. 대한민국 또는 NobleProg 기업 교육 센터의 대한민국 . 원격 라이브 교육은 대화식 원격 데스크톱을 통해 수행됩니다. NobleProg-현지 교육 제공 업체

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Kubeflow코스 개요

코스 이름
Duration
Overview
코스 이름
Duration
Overview
35 시간
Overview
This instructor-led, live training in 대한민국 (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.
28 시간
Overview
This instructor-led, live training in 대한민국 (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.
28 시간
Overview
This instructor-led, live training in 대한민국 (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.
28 시간
Overview
This instructor-led, live training in 대한민국 (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.
28 시간
Overview
This instructor-led, live training in 대한민국 (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.
28 시간
Overview
This instructor-led, live training in 대한민국 (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.
28 시간
Overview
This instructor-led, live training in 대한민국 (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.

향후Kubeflow 코스

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