Helyi, oktató által vezetett élő Kubeflow képzések bizonyítaniuk interaktív gyakorlati feladatok, hogyan kell használni Kubeflow létrehozását, telepítését, és kezelheti a gépi tanulási munkafolyamatokat a Kubernetes . Kubeflow edzés „helyszíni élő Kubeflow ” vagy „távoli élő Kubeflow ” érhető el. A helyszíni élő képzéseket helyi ügyfelek területén lehet végrehajtani Magyarország vagy a NobleProg vállalati képzési központjain Magyarország . A távoli élő képzést interaktív, távoli asztalon végzik. NobleProg - a helyi képzési szolgáltató
Machine Translated
Vélemények
Az oktató nagyon interaktív, érzékeny és hozzáértő.
Credit Suisse
Kurzus: Docker and Kubernetes
Machine Translated
The labs and the discussion.
Synchronoss Software Ireland Limited
Kurzus: Docker and Kubernetes
Relaxed, easy going pace.
Synchronoss Software Ireland Limited
Kurzus: Docker and Kubernetes
The exercise VMs were working quite well.
Philippe DEPRUGNEY - Synchronoss Software Ireland Limited
Kurzus: Docker and Kubernetes
Resources preparation (VMs and such) and the ability to answer questions
Synchronoss Software Ireland Limited
Kurzus: Docker and Kubernetes
Kamil's pleasant way backed by his knowledge on the subject matter was the best part of this course
Synchronoss Software Ireland Limited
Kurzus: Docker and Kubernetes
The trainer was extremely approachable and helpful and was able to solve any issues that we encountered in the class. Even after he had finished all of the planned course material he still stayed on late to answer questions some people in the course had.
Synchronoss Software Ireland Limited
Kurzus: Docker and Kubernetes
The hands on exercises
Synchronoss Software Ireland Limited
Kurzus: Docker and Kubernetes
Kamil mind a Docker és a Kubernetes mind a 3 nap alatt magabiztos felhasználókhoz (ha nem KKV-k!) Hozott minket. Mindig megközelíthetően és türelmesen válaszolt a kérdéseimre. Igazi szelíd.
Patrick Kennedy - Synchronoss Software Ireland Limited
Kurzus: Docker and Kubernetes
Machine Translated
Exercises, practical questions answered clearly
Mayeul Marguet - Synchronoss Software Ireland Limited
Kurzus: Docker and Kubernetes
Go od mix gyakorlati és elmélet, Kamil adta jól célzott képzési szabott igényeinknek, bár a képzés tartalma igen széles 3 napon át.
Synchronoss Software Ireland Limited
Kurzus: Docker and Kubernetes
Machine Translated
The subject, and trainer knowledge
Thiqha Business Services
Kurzus: Docker, Kubernetes and OpenShift for Developers
Hogy Brian jól ismeri a témát, és jól magyarázza
Francisco Demetrio Quitral - IMED S.A
Kurzus: Managing Kubernetes with Rancher
Machine Translated
megmutattuk, hogy a kubectl parancsai hogyan tükröződnek a rancher interfészben és az AWS szolgáltatásokban, ami nagyon világossá tette a különféle alkatrészek kölcsönhatásait.
IMED S.A
Kurzus: 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
Kurzus: Docker and Kubernetes
The exercises
Van Tonilin Ramos - Temic Automotive Philippines
Kurzus: Docker and Kubernetes
All
Eduardo Darroca Jr - Temic Automotive Philippines
Kurzus: 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
Kurzus: Docker and Kubernetes
minden
Mikael Larsson Österström - Bolagsverket
Kurzus: Docker and Kubernetes
Machine Translated
The teacher has worked with the subject
Bolagsverket
Kurzus: Docker and Kubernetes
The material in general. The trainer was very knowledgable.
MDA Systems Ltd.
Kurzus: Kubernetes Advanced
Az oktató módja annak, hogy elmagyarázza a témát, és ki tegye nagyon egyszerűvé a koncepciót.
Faten AlDawish - TAMKEEN TECHNOLOGIES COMPANY
Kurzus: Docker and Kubernetes: Building and Scaling a Containerized Application
Machine Translated
laboratóriumok és kétségessé teszik a tisztázást
venkata velpuri - Charter Communications INC
Kurzus: Kubernetes from Basic to Advanced
Machine Translated
Gyakorlati oktatás.
Charter Communications INC
Kurzus: Kubernetes from Basic to Advanced
Machine Translated
Az edzés során nem érezte unatkozást.
Charter Communications INC
Kurzus: 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
Kurzus: Docker and Kubernetes
Tetszettek a példák, amelyek kiegészítették az elméletet a bemutatón.
Miłosz Gałązka - LPP S.A.
Kurzus: 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
Kurzus: Docker and Kubernetes
Kamil's attitude and knowledge. Very friendly, highly knowledgeable about the subject, great listener.
TriOptima AB
Kurzus: 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++.
Kurzus: Docker and Kubernetes
the intro and the structure of the slides
Abraxas Informatik AG
Kurzus: Managing Kubernetes with Rancher
Számos gyakorlat és beállítás a gyakorlatban.
Atos Poland Global Services spółka z ograniczoną odpowiedzialnością
Kurzus: Docker and Kubernetes
Machine Translated
Deep expertise of the faculty
Kurzus: 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
Kurzus: 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++.
Kurzus: Docker and Kubernetes
Deep expertise of the faculty
Kurzus: Creating a Service Mesh with Istio and Kubernetes
Kubeflow Course Outlines
Kurzusnév
Időtartalma
Összefoglaló
Kurzusnév
Időtartalma
Összefoglaló
35 hours
Összefoglaló
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
Összefoglaló
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
Összefoglaló
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
Összefoglaló
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
Összefoglaló
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
Összefoglaló
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
Összefoglaló
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
Upcoming Kubeflow Courses
2020-07-07 09:30:00
Miskolc
28 hours
2020-07-27 09:30:00
Debrecen
28 hours
2020-09-29 09:30:00
Debrecen
28 hours
2020-10-14 09:30:00
Miskolc
28 hours
2020-11-24 09:30:00
Debrecen
28 hours
Other countries
Consulting
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