Lokale Live- Kubeflow Schulungskurse, die von Lehrern geleitet werden, demonstrieren anhand interaktiver praktischer Übungen, wie Kubeflow zum Erstellen, Bereitstellen und Verwalten von Workflows für maschinelles Lernen auf Kubernetes . Kubeflow Training ist als "Onsite-Live-Training" oder "Remote-Live-Training" verfügbar. Vor-Ort-Live-Schulungen können vor Ort beim Kunden in Berlin durchgeführt werden Schweiz oder in NobleProg Firmenschulungszentren in Schweiz . Das Remote-Live-Training erfolgt über einen interaktiven Remote-Desktop. NobleProg - Ihr lokaler Schulungsanbieter
Machine Translated
Erfahrungsberichte
Kubernetes Architektur
John Howlett
Kurs: Docker and Kubernetes
Adjusting to our needs
Sumitomo Mitsui Finance and Leasing Company, Limited
Kurs: Kubeflow
Go od Mischung aus praktischen Übungen und Theorie, gab Kamil fokussierte Ausbildung gut auf unsere Bedürfnisse zugeschnitten, obwohl der Kursinhalt für 3 Tage sehr breit ist.
Synchronoss Software Ireland Limited
Kurs: Docker and Kubernetes
Machine Translated
The subject, and trainer knowledge
Thiqha Business Services
Kurs: Docker, Kubernetes and OpenShift for Developers
Dass Brian das Thema gut kennt und gut erklärt
Francisco Demetrio Quitral - IMED S.A
Kurs: Managing Kubernetes with Rancher
Machine Translated
Es wurde uns gezeigt, wie sich die Kubectl-Befehle in der Rancher-Oberfläche und in den AWS-Diensten widerspiegeln, wodurch die Interaktion der verschiedenen Teile sehr deutlich wurde.
IMED S.A
Kurs: Managing Kubernetes with Rancher
Machine Translated
The techical knowlegde of the trainer about the subject so he can answer most of the questions.
Synchronoss Software Ireland Limited
Kurs: Docker and Kubernetes
The exercises
Van Tonilin Ramos - Temic Automotive Philippines
Kurs: Docker and Kubernetes
All
Eduardo Darroca Jr - Temic Automotive Philippines
Kurs: Docker and Kubernetes
It never felt like the pace was high but we still managed to cover many topics in only three days. I liked that Adriano did not stop the lecture to have us do labs. The style of trying out commands in the lab environment while watching and listening to the lecture was good in my opinion. Adriano was very flexible to answer questions that came up without getting distracted too much from the current topic.
Bolagsverket
Kurs: Docker and Kubernetes
alle
Mikael Larsson Österström - Bolagsverket
Kurs: Docker and Kubernetes
Machine Translated
The teacher has worked with the subject
Bolagsverket
Kurs: Docker and Kubernetes
The material in general. The trainer was very knowledgable.
MDA Systems Ltd.
Kurs: Kubernetes Advanced
Die Art und Weise, wie der Trainer das Thema erklärt und wer das Konzept sehr einfach macht.
Faten AlDawish - TAMKEEN TECHNOLOGIES COMPANY
Kurs: Docker and Kubernetes: Building and Scaling a Containerized Application
Machine Translated
Labor und Zweifel Aufklärung
venkata velpuri - Charter Communications INC
Kurs: Kubernetes from Basic to Advanced
Machine Translated
Handson Training.
Charter Communications INC
Kurs: Kubernetes from Basic to Advanced
Machine Translated
Habe mich während des Trainings nicht gelangweilt gefühlt.
Charter Communications INC
Kurs: Kubernetes from Basic to Advanced
Machine Translated
Fantastic delivery of the training, really enjoyable and all round good vibe throughout
Chris Brant - Hewlett Packard Enterprise
Kurs: Docker and Kubernetes
Mir gefielen die Beispiele, die die Theorie bei der Präsentation ergänzten.
Miłosz Gałązka - LPP S.A.
Kurs: OpenShift for Administrators
Machine Translated
We did small examples. I have seen a lot of huge config files and they always looked overwhelming. It was really nice to see small examples!
Marcus Nordberg - TriOptima AB
Kurs: Docker and Kubernetes
Kamil's attitude and knowledge. Very friendly, highly knowledgeable about the subject, great listener.
TriOptima AB
Kurs: Docker and Kubernetes
Kamil's expertise and patience while guiding me through some complex topics. His ability to spontaneously answer some difficult questions that I had about the material. I'd give him an A++.
Kurs: Docker and Kubernetes
the intro and the structure of the slides
Abraxas Informatik AG
Kurs: Managing Kubernetes with Rancher
Eine große Anzahl von Übungen und Einstellung auf Übung.
Atos Poland Global Services spółka z ograniczoną odpowiedzialnością
Kurs: Docker and Kubernetes
Machine Translated
Deep expertise of the faculty
Kurs: Creating a Service Mesh with Istio and Kubernetes
Kamil knows a lot about Kubernetes, and gives the impression he really enjoys working with it. He was able to give sessions and tasks for us to do based off of our requests and suggestions. Troubleshooting was deliberately built in to some exercizes - that was really useful too. The lab environment worked great.
Donald MacPhee - Origo
Kurs: Docker and Kubernetes
lot of information.
Global Knowledge Network Training Ltd
Kurs: Docker and Kubernetes
The training was in details with lots of practical example demo.
Juvena Yashmin - Global Knowledge Network Training Ltd
Kurs: Docker and Kubernetes
The knowledge of the trainer about the subject
Global Knowledge Network Training Ltd
Kurs: Docker and Kubernetes
got thorough knowledge of the container and kubernetes concepts
Global Knowledge Network Training Ltd
Kurs: Docker and Kubernetes
content prepared and shared are very much useful and we can't find them in google. so i am happy with that.
Mohamed Abubucker - Geidea
Kurs: Docker and Kubernetes
new system and technology to lear
Geidea
Kurs: Docker and Kubernetes
Trainer has broad knowledge and about subjects, the way he answer all the question and his patience of listening audience is highly appreciable . Trainer never always ready to stay with audience apart from given hours.. appreciate it.
Sourav Singh - Geidea
Kurs: Docker and Kubernetes
Lab session.
Faiyaz Ahmad Alam - Geidea
Kurs: Docker and Kubernetes
Lösung gemeinsamer Probleme, Präsentation interessanter Anwendungen
Marcin Kowalczyk
Kurs: Certified Kubernetes Administrator (CKA) - exam preparation
Machine Translated
Kamil's expertise and patience while guiding me through some complex topics. His ability to spontaneously answer some difficult questions that I had about the material. I'd give him an A++.
Kurs: Docker and Kubernetes
Deep expertise of the faculty
Kurs: Creating a Service Mesh with Istio and Kubernetes
Kubeflow Kurspläne
Name des Kurses
Dauer
Überblick
Name des Kurses
Dauer
Überblick
35 hours
Überblick
Kubeflow is a toolkit for making Machine Learning (ML) on Kubernetes easy, portable and scalable. AWS EKS (Elastic Kubernetes Service) is an Amazon managed service for running the Kubernetes on AWS.
This instructor-led, live training (onsite or remote) 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.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
This instructor-led, live training (onsite or remote) 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.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
28 hours
Überblick
Kubeflow is a framework for running Machine Learning workloads on Kubernetes. TensorFlow is a machine learning library and Kubernetes is an orchestration platform for managing containerized applications.
This instructor-led, live training (onsite or remote) 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.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
This instructor-led, live training (onsite or remote) 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.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
28 hours
Überblick
Kubeflow is a framework for running Machine Learning workloads on Kubernetes. TensorFlow is one of the most popular machine learning libraries. Kubernetes is an orchestration platform for managing containerized applications.
This instructor-led, live training (onsite or remote) 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.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
This instructor-led, live training (onsite or remote) 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.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
28 hours
Überblick
Kubeflow is a framework for running Machine Learning workloads on Kubernetes. TensorFlow is one of the most popular machine learning libraries. Kubernetes is an orchestration platform for managing containerized applications.
This instructor-led, live training (onsite or remote) 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.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
This instructor-led, live training (onsite or remote) 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.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
28 hours
Überblick
Kubeflow is a framework for running Machine Learning workloads on Kubernetes. TensorFlow is one of the most popular machine learning libraries. Kubernetes is an orchestration platform for managing containerized applications.
This instructor-led, live training (onsite or remote) 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.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
This instructor-led, live training (onsite or remote) 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.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
28 hours
Überblick
Kubeflow is a framework for running Machine Learning workloads on Kubernetes. TensorFlow is one of the most popular machine learning libraries. Kubernetes is an orchestration platform for managing containerized applications. OpenShift is an cloud application development platform that uses Docker containers, orchestrated and managed by Kubernetes, on a foundation of Red Hat Enterprise Linux.
This instructor-led, live training (onsite or remote) 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.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
This instructor-led, live training (onsite or remote) 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.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
28 hours
Überblick
Kubeflow is a toolkit for making Machine Learning (ML) on Kubernetes easy, portable and scalable.
This instructor-led, live training (onsite or remote) 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.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
- To learn more about Kubeflow, please visit: https://github.com/kubeflow/kubeflow
This instructor-led, live training (onsite or remote) 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.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
- To learn more about Kubeflow, please visit: https://github.com/kubeflow/kubeflow
Other countries
Consulting
Kubeflow Schulung, Kubeflow boot camp, Kubeflow Abendkurse, Kubeflow Wochenendkurse, Kubeflow Kurs, Kubeflow Training, Kubeflow Seminar, Kubeflow Seminare, Kubeflow Privatkurs, Kubeflow Coaching, Kubeflow Lehrer
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