I corsi di formazione Kubeflow dal vivo condotti da istruttori locali dimostrano attraverso pratiche pratiche interattive come utilizzare Kubeflow per costruire, distribuire e gestire flussi di lavoro di machine learning su Kubernetes . Kubeflow allenamento Kubeflow è disponibile come "allenamento dal vivo in loco" o "allenamento dal vivo a distanza". La formazione dal vivo in loco può essere svolta localmente presso la sede del cliente in Italia o nei centri di formazione aziendale NobleProg in Italia . La formazione dal vivo a distanza viene effettuata tramite un desktop remoto interattivo. NobleProg - Il tuo provider di formazione locale
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Corso: Kubeflow
Schema generale del corso Kubeflow
Nome del corso
Durata
Overview
Nome del corso
Durata
Overview
35 hours
Overview
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 (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.
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 (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.
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
Overview
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 (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.
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 (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.
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
Overview
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 (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.
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 (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.
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
Overview
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 (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.
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 (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.
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
Overview
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 (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.
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 (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.
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
Overview
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 (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.
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 (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.
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
Overview
Kubeflow is a toolkit for making Machine Learning (ML) on Kubernetes easy, portable and scalable.
This instructor-led, live training (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.
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 (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.
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
Prossimi corsi Kubeflow
2020-10-19 09:30:00
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2020-11-17 09:30:00
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2020-12-14 09:30:00
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2021-01-11 09:30:00
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2021-03-09 09:30:00
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Other countries
Consulting
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