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
Beállítása igényeinkhez
Sumitomo Mitsui Finance and Leasing Company, Limited
Kurzus: Kubeflow
Machine Translated
Kubeflow Course Outlines
Kurzusnév
Időtartalma
Összefoglaló
Kurzusnév
Időtartalma
Összefoglaló
35 hours
This instructor-led, live training in Magyarország (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.
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 hours
This instructor-led, live training in Magyarország (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.
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 hours
This instructor-led, live training in Magyarország (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.
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 hours
This instructor-led, live training in Magyarország (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.
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 hours
This instructor-led, live training in Magyarország (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.
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 hours
This instructor-led, live training in Magyarország (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.
- 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 hours
This instructor-led, live training in Magyarország (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.
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.
Last Updated:
Upcoming Kubeflow Courses
21-12-2021 09:30
28 hours
17-01-2022 09:30
28 hours
25-01-2022 09:30
28 hours
08-02-2022 09:30
28 hours
08-02-2022 09:30
28 hours
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