Místní tréninkové kurzy Kubeflow instruktory ukazují prostřednictvím interaktivních praktických cvičení, jak používat Kubeflow k vytváření, nasazení a správě pracovních postupů strojového učení na Kubernetes . Kubeflow je k dispozici jako „živý trénink na místě“ nebo „vzdálený živý trénink“. Živé školení na místě lze provádět místně v prostorách zákazníka v České republice nebo ve firemních školicích střediscích NobleProg v České republice . Vzdálené živé školení se provádí prostřednictvím interaktivní vzdálené plochy. NobleProg - váš místní poskytovatel školení
Machine Translated
Reference
Adjusting to our needs
Sumitomo Mitsui Finance and Leasing Company, Limited
Kurz: Kubeflow
The hands on exercises
Synchronoss Software Ireland Limited
Kurz: Docker and Kubernetes
Kamil nás přivedl všechny od nováčků Docker a Kubernetes k sebevědomým uživatelům (ne-li malým a středním podnikům!) Za pouhé 3 dny. Vždy odpovídal na mé otázky a byl trpělivý. Opravdu gentleman.
Patrick Kennedy - Synchronoss Software Ireland Limited
Kurz: Docker and Kubernetes
Machine Translated
Exercises, practical questions answered clearly
Mayeul Marguet - Synchronoss Software Ireland Limited
Kurz: Docker and Kubernetes
Go kombinace praktických cvičení a teorie, Kamil poskytl dobře zaměřené školení přizpůsobené našim potřebám, i když obsah kurzu je velmi široký po dobu 3 dnů.
Synchronoss Software Ireland Limited
Kurz: Docker and Kubernetes
Machine Translated
The subject, and trainer knowledge
Thiqha Business Services
Kurz: Docker, Kubernetes and OpenShift for Developers
Že Brian má dobrou znalost předmětu a dobře vysvětluje
Francisco Demetrio Quitral - IMED S.A
Kurz: Managing Kubernetes with Rancher
Machine Translated
že nám bylo ukázáno, jak se příkazy kubectl odrážejí v rozhraní rančerů a službách AWS, což jasně ukázalo, jak různé části spolupracují.
IMED S.A
Kurz: 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
Kurz: Docker and Kubernetes
The exercises
Van Tonilin Ramos - Temic Automotive Philippines
Kurz: Docker and Kubernetes
All
Eduardo Darroca Jr - Temic Automotive Philippines
Kurz: 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
Kurz: Docker and Kubernetes
Všechno
Mikael Larsson Österström - Bolagsverket
Kurz: Docker and Kubernetes
Machine Translated
The teacher has worked with the subject
Bolagsverket
Kurz: Docker and Kubernetes
The material in general. The trainer was very knowledgable.
MDA Systems Ltd.
Kurz: Kubernetes Advanced
Trenérův způsob, jak vysvětlit téma a kdo učiní koncept velmi jednoduchým.
Faten AlDawish - TAMKEEN TECHNOLOGIES COMPANY
Kurz: Docker and Kubernetes: Building and Scaling a Containerized Application
Machine Translated
laboratoře a vyjasnění pochybností
venkata velpuri - Charter Communications INC
Kurz: Kubernetes from Basic to Advanced
Machine Translated
Handsonský výcvik.
Charter Communications INC
Kurz: Kubernetes from Basic to Advanced
Machine Translated
Cítil jsem se, když se během tréninku nudil.
Charter Communications INC
Kurz: 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
Kurz: Docker and Kubernetes
Líbily se mi příklady, které tuto teorii na prezentaci doplnily.
Miłosz Gałązka - LPP S.A.
Kurz: 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
Kurz: Docker and Kubernetes
Kamil's attitude and knowledge. Very friendly, highly knowledgeable about the subject, great listener.
TriOptima AB
Kurz: 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++.
Kurz: Docker and Kubernetes
the intro and the structure of the slides
Abraxas Informatik AG
Kurz: Managing Kubernetes with Rancher
Velké množství cvičení a nastavení na praxi.
Atos Poland Global Services spółka z ograniczoną odpowiedzialnością
Kurz: Docker and Kubernetes
Machine Translated
Deep expertise of the faculty
Kurz: 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
Kurz: Docker and Kubernetes
lot of information.
Global Knowledge Network Training Ltd
Kurz: Docker and Kubernetes
The training was in details with lots of practical example demo.
Juvena Yashmin - Global Knowledge Network Training Ltd
Kurz: Docker and Kubernetes
The knowledge of the trainer about the subject
Global Knowledge Network Training Ltd
Kurz: Docker and Kubernetes
got thorough knowledge of the container and kubernetes concepts
Global Knowledge Network Training Ltd
Kurz: 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
Kurz: Docker and Kubernetes
new system and technology to lear
Geidea
Kurz: 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
Kurz: 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++.
Kurz: Docker and Kubernetes
Deep expertise of the faculty
Kurz: Creating a Service Mesh with Istio and Kubernetes
Kubeflow Návrh Školení
Název školení
Doba trvání
Přehled
Název školení
Doba trvání
Přehled
35 hodin
Přehled
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 hodin
Přehled
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 hodin
Přehled
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 hodin
Přehled
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 hodin
Přehled
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 hodin
Přehled
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 hodin
Přehled
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
Nadcházející Kubeflow Školení
2020-06-30 09:30:00
Praha, Pražský hrad
28 hodiny
2020-07-14 09:30:00
Praha, Pražský hrad
28 hodiny
2020-09-02 09:30:00
Praha, Pražský hrad
28 hodiny
2020-09-23 09:30:00
Praha, Pražský hrad
28 hodiny
2020-11-18 09:30:00
Praha, Pražský hrad
28 hodiny
Ostatní regiony
Ostatní země
Konzultace
Víkendové Kubeflow kurzy, Večerní Kubeflow školení, Kubeflow přijímač, Kubeflow vedené školitelem, Víkendové Kubeflow školení, Večerní Kubeflow kurzy, Kubeflow koučování, Kubeflow lektor, Kubeflow školitel, Kubeflow počítačová školení, Kubeflow počítačové kurzy , Kubeflow kurzy, Kubeflow školení, Kubeflow on-site, Kubeflow uzavřená školení, Kubeflow individuální školení
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